Machine-generated data is one of the fastest growing and complex areas of big data. It's also one of the most valuable, containing some of the most important insights: where things went wrong, how to optimize the customer experience, the fingerprints of fraud. Join us as we explore the basics of machine data analysis and highlight techniques to help you turn your organization’s machine data into valuable insights—across IT and the business. This introductory workshop includes a hands-on (bring your laptop) demonstration of Splunk’s technology and covers use cases both inside and outside IT. Learn why more than 13,000 customers in over 110 countries use Splunk to make their organizations more efficient, secure, and profitable.
The document discusses elastic data warehousing using Snowflake's cloud-based data warehouse as a service. Traditional data warehousing and NoSQL solutions are costly and complex to manage. Snowflake provides a fully managed elastic cloud data warehouse that can scale instantly. It allows consolidating all data in one place and enables fast analytics on diverse data sources at massive scale, without the infrastructure complexity or management overhead of other solutions. Customers have realized significantly faster analytics, lower costs, and the ability to easily add new workloads compared to their previous data platforms.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Snowflake is a cloud-based data warehouse that is built for the cloud. It was founded in 2012 and has raised $1 billion in funding. Snowflake's architecture separates storage, compute, and metadata services, allowing it to offer unlimited scalability, multiple clusters that can access shared data with no downtime, and full transactional consistency across the system. Snowflake has over 2000 customers including large enterprises that use it for analytics, data science, and sharing large volumes of data securely.
This document discusses building the data lakehouse, a new data architecture that combines the best of data warehouses and data lakes. It introduces the concept of the data lakehouse and compares it to traditional data warehouses and data lakes. The data lakehouse stores all types of data in an open, cloud-based environment and uses machine learning and analytics to deliver insights. It provides a single platform for various users and workloads, from data engineering to data science.
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
This document discusses upgrading an existing Active Directory environment from Windows Server 2003 to Windows Server 2008 R2, including new features in Windows Server 2008 R2, server version requirements, installation options, recommended upgrade scenarios to address, and considerations for time configuration, network ports, Kerberos encryption changes, and other known issues.
DLP Systems: Models, Architecture and AlgorithmsLiwei Ren任力偉
DLP is a data security technology that detects and prevents data breach incidents by monitoring data in-use, in-motion and at-rest. It has been widely applied for regulatory compliances, data privacy and intellectual property protection. This talk will introduce basic concepts and security models to describe DLP systems with high level architecture. DLP is an interesting discipline with content inspection techniques supported by sophisticated algorithms. Special investigation will be taken for a few algorithms: document fingerprinting, data record fingerprinting, scalable M-pattern string match and etc..
This document discusses application threat modeling. It begins with introducing key terminology used in threat modeling like assets, threats, attacks, and risks. It then explains what threat modeling is and when it should be performed. The document outlines three main approaches to threat modeling: asset-centric, attacker-centric using attack trees, and system-centric. It provides examples of each approach and discusses how to identify threats, calculate risks, and plan countermeasures as part of the system-centric threat modeling process.
The document discusses elastic data warehousing using Snowflake's cloud-based data warehouse as a service. Traditional data warehousing and NoSQL solutions are costly and complex to manage. Snowflake provides a fully managed elastic cloud data warehouse that can scale instantly. It allows consolidating all data in one place and enables fast analytics on diverse data sources at massive scale, without the infrastructure complexity or management overhead of other solutions. Customers have realized significantly faster analytics, lower costs, and the ability to easily add new workloads compared to their previous data platforms.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Snowflake is a cloud-based data warehouse that is built for the cloud. It was founded in 2012 and has raised $1 billion in funding. Snowflake's architecture separates storage, compute, and metadata services, allowing it to offer unlimited scalability, multiple clusters that can access shared data with no downtime, and full transactional consistency across the system. Snowflake has over 2000 customers including large enterprises that use it for analytics, data science, and sharing large volumes of data securely.
This document discusses building the data lakehouse, a new data architecture that combines the best of data warehouses and data lakes. It introduces the concept of the data lakehouse and compares it to traditional data warehouses and data lakes. The data lakehouse stores all types of data in an open, cloud-based environment and uses machine learning and analytics to deliver insights. It provides a single platform for various users and workloads, from data engineering to data science.
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
This document discusses upgrading an existing Active Directory environment from Windows Server 2003 to Windows Server 2008 R2, including new features in Windows Server 2008 R2, server version requirements, installation options, recommended upgrade scenarios to address, and considerations for time configuration, network ports, Kerberos encryption changes, and other known issues.
DLP Systems: Models, Architecture and AlgorithmsLiwei Ren任力偉
DLP is a data security technology that detects and prevents data breach incidents by monitoring data in-use, in-motion and at-rest. It has been widely applied for regulatory compliances, data privacy and intellectual property protection. This talk will introduce basic concepts and security models to describe DLP systems with high level architecture. DLP is an interesting discipline with content inspection techniques supported by sophisticated algorithms. Special investigation will be taken for a few algorithms: document fingerprinting, data record fingerprinting, scalable M-pattern string match and etc..
This document discusses application threat modeling. It begins with introducing key terminology used in threat modeling like assets, threats, attacks, and risks. It then explains what threat modeling is and when it should be performed. The document outlines three main approaches to threat modeling: asset-centric, attacker-centric using attack trees, and system-centric. It provides examples of each approach and discusses how to identify threats, calculate risks, and plan countermeasures as part of the system-centric threat modeling process.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
Azure Data Factory Mapping Data Flow allows users to stage and transform data in Azure during a limited preview period beginning in February 2019. Data can be staged from Azure Data Lake Storage, Blob Storage, or SQL databases/data warehouses, then transformed using visual data flows before being landed to staging areas in Azure like ADLS, Blob Storage, or SQL databases. For information, contact adfdataflowext@microsoft.com or visit http://aka.ms/dataflowpreview.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
This document provides an overview of data loss prevention (DLP) technology. It discusses what DLP is, different DLP models for data in use, in motion, and at rest. It also covers typical DLP system architecture, approaches for data classification and identification, and some technical challenges. The document references DLP product websites and summarizes two research papers on using machine learning for automatic text classification to identify sensitive data for DLP systems.
Data Loss Prevention (DLP) - Fundamental Concept - ErykEryk Budi Pratama
This document discusses data loss prevention (DLP) concepts and implementations. It begins with an overview of data governance and the data lifecycle. It then defines DLP, explaining how DLP solutions protect data in motion, at rest, and in use. Sample DLP deployments are shown, outlining key activities and considerations for implementation such as governance, infrastructure, and a phased approach. Finally, examples of DLP use cases are provided for data in motion like email and data in use on workstations.
Snowflake's Kent Graziano talks about what makes a data warehouse as a service and some of the key features of Snowflake's data warehouse as a service.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
This document provides an overview and agenda for a Splunk Machine Data Workshop. It discusses Splunk's approach to machine data, its industry-leading platform capabilities, and covers topics including non-traditional data sources, data enrichment, advanced search and reporting commands, data models and pivots, custom visualizations, and workshop setup instructions. Attendees will learn how to index sample data, perform searches, detect patterns, and explore non-traditional data sources.
This document discusses information security and its key aspects. It defines security as protection from danger and adversaries. There are multiple layers of security including physical, personal, operations, communications, and network security. Information security protects information, systems, and hardware that store, transmit, and use information. Critical characteristics of information that require protection are availability, accuracy, authenticity, confidentiality, integrity, utility, and possession. Security types include physical, personal, operations, communications, network, and information security. Risk is defined as the possibility that a threat exploits a vulnerability, where threats are things that can cause damage and vulnerabilities are weaknesses that can be exploited.
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Mapping Data Flows Training deck Q1 CY22Mark Kromer
Mapping data flows allow for code-free data transformation at scale using an Apache Spark engine within Azure Data Factory. Key points:
- Mapping data flows can handle structured and unstructured data using an intuitive visual interface without needing to know Spark, Scala, Python, etc.
- The data flow designer builds a transformation script that is executed on a JIT Spark cluster within ADF. This allows for scaled-out, serverless data transformation.
- Common uses of mapping data flows include ETL scenarios like slowly changing dimensions, analytics tasks like data profiling, cleansing, and aggregations.
The document appears to be a presentation by Splunk Inc. discussing their data platform. Some key points:
1. Splunk's platform allows customers to investigate, monitor, analyze and act on data from any source in real-time.
2. It addresses challenges of collecting and making sense of massive amounts of data from various systems and devices across IT, security, and IoT use cases.
3. Splunk provides solutions and services to help customers accelerate their data journey from initial investigation to taking action.
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
Most organisations think that they have poor data quality, but don’t know how to measure it or what to do about it. Teams of data scientists, analysts, and ETL developers are either blindly taking a “garbage in -> garbage out” approach, or worse still, “cleansing” data to fit their limited perspectives. DataOps is a systematic approach to measuring data and for planning mitigations for bad data.
Data Warehousing in the Cloud: Practical Migration Strategies SnapLogic
Dave Wells of Eckerson Group discusses why cloud data warehousing has become popular, the many benefits, and the corresponding challenges. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema, or rebuilding of data pipelines is needed.
7 Experts on Implementing Microsoft Defender for EndpointMighty Guides, Inc.
1) Before implementing Microsoft Defender for Endpoint, experts recommend learning how the tool works by creating a lab environment and testing it with attack simulations.
2) When first deploying the tool, start with a baseline configuration and one test machine to familiarize yourself with all settings and configurations.
3) Ongoing monitoring and responding to alerts is important for the tool's machine learning capabilities to improve over time at detecting threats in the environment. User buy-in is also important as some initial false positives may occur.
Cisco ASA Firewall Presentation - ZABTech center HyderabadMehtabRohela
Cisco ASA is a network security appliance that combines firewall, antivirus, intrusion prevention, and VPN capabilities. It provides threat defense by monitoring network traffic and can deny or permit access between internal and external networks. Key features include packet filtering, network address translation, application inspection, VPN support, and high availability options. The ASA can operate in routed or transparent firewall modes and supports authentication, dynamic routing, clustering, and next-generation firewall features like advanced malware protection. It is suitable for both small and large networks due to scalability and modular design.
EventCombMT is a highly-configurable tool that writes system events to the Windows Event Log. It is part of the Sysinternals suite and is under active development. EventCombMT is useful for security, support, and IT teams to monitor system activity through the Windows Event Log. It allows filtering of event log entries through configurable rule groups.
The document provides an overview of Splunk's machine data platform and capabilities for collecting, analyzing, and visualizing machine data from various sources. It discusses Splunk's approaches to machine data including universal indexing and schema-on-the-fly. It also covers Splunk's portfolio including apps, add-ons, and premium solutions. Finally, it discusses various methods for collecting non-traditional data sources such as network inputs, HTTP Event Collector, log event alerts, Splunk Stream, scripted inputs, database inputs, and modular inputs.
This document provides an overview and agenda for a Splunk Machine Data Workshop 101 session on data enrichment techniques in Splunk including tags, field aliases, calculated fields, and lookups. It discusses how these features add context and meaning to raw machine data by labeling, normalizing, and augmenting data. Examples are given of creating and applying each enrichment method and searching events with the enriched fields.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
Azure Data Factory Mapping Data Flow allows users to stage and transform data in Azure during a limited preview period beginning in February 2019. Data can be staged from Azure Data Lake Storage, Blob Storage, or SQL databases/data warehouses, then transformed using visual data flows before being landed to staging areas in Azure like ADLS, Blob Storage, or SQL databases. For information, contact adfdataflowext@microsoft.com or visit http://aka.ms/dataflowpreview.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
This document provides an overview of data loss prevention (DLP) technology. It discusses what DLP is, different DLP models for data in use, in motion, and at rest. It also covers typical DLP system architecture, approaches for data classification and identification, and some technical challenges. The document references DLP product websites and summarizes two research papers on using machine learning for automatic text classification to identify sensitive data for DLP systems.
Data Loss Prevention (DLP) - Fundamental Concept - ErykEryk Budi Pratama
This document discusses data loss prevention (DLP) concepts and implementations. It begins with an overview of data governance and the data lifecycle. It then defines DLP, explaining how DLP solutions protect data in motion, at rest, and in use. Sample DLP deployments are shown, outlining key activities and considerations for implementation such as governance, infrastructure, and a phased approach. Finally, examples of DLP use cases are provided for data in motion like email and data in use on workstations.
Snowflake's Kent Graziano talks about what makes a data warehouse as a service and some of the key features of Snowflake's data warehouse as a service.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
This document provides an overview and agenda for a Splunk Machine Data Workshop. It discusses Splunk's approach to machine data, its industry-leading platform capabilities, and covers topics including non-traditional data sources, data enrichment, advanced search and reporting commands, data models and pivots, custom visualizations, and workshop setup instructions. Attendees will learn how to index sample data, perform searches, detect patterns, and explore non-traditional data sources.
This document discusses information security and its key aspects. It defines security as protection from danger and adversaries. There are multiple layers of security including physical, personal, operations, communications, and network security. Information security protects information, systems, and hardware that store, transmit, and use information. Critical characteristics of information that require protection are availability, accuracy, authenticity, confidentiality, integrity, utility, and possession. Security types include physical, personal, operations, communications, network, and information security. Risk is defined as the possibility that a threat exploits a vulnerability, where threats are things that can cause damage and vulnerabilities are weaknesses that can be exploited.
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Mapping Data Flows Training deck Q1 CY22Mark Kromer
Mapping data flows allow for code-free data transformation at scale using an Apache Spark engine within Azure Data Factory. Key points:
- Mapping data flows can handle structured and unstructured data using an intuitive visual interface without needing to know Spark, Scala, Python, etc.
- The data flow designer builds a transformation script that is executed on a JIT Spark cluster within ADF. This allows for scaled-out, serverless data transformation.
- Common uses of mapping data flows include ETL scenarios like slowly changing dimensions, analytics tasks like data profiling, cleansing, and aggregations.
The document appears to be a presentation by Splunk Inc. discussing their data platform. Some key points:
1. Splunk's platform allows customers to investigate, monitor, analyze and act on data from any source in real-time.
2. It addresses challenges of collecting and making sense of massive amounts of data from various systems and devices across IT, security, and IoT use cases.
3. Splunk provides solutions and services to help customers accelerate their data journey from initial investigation to taking action.
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
Most organisations think that they have poor data quality, but don’t know how to measure it or what to do about it. Teams of data scientists, analysts, and ETL developers are either blindly taking a “garbage in -> garbage out” approach, or worse still, “cleansing” data to fit their limited perspectives. DataOps is a systematic approach to measuring data and for planning mitigations for bad data.
Data Warehousing in the Cloud: Practical Migration Strategies SnapLogic
Dave Wells of Eckerson Group discusses why cloud data warehousing has become popular, the many benefits, and the corresponding challenges. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema, or rebuilding of data pipelines is needed.
7 Experts on Implementing Microsoft Defender for EndpointMighty Guides, Inc.
1) Before implementing Microsoft Defender for Endpoint, experts recommend learning how the tool works by creating a lab environment and testing it with attack simulations.
2) When first deploying the tool, start with a baseline configuration and one test machine to familiarize yourself with all settings and configurations.
3) Ongoing monitoring and responding to alerts is important for the tool's machine learning capabilities to improve over time at detecting threats in the environment. User buy-in is also important as some initial false positives may occur.
Cisco ASA Firewall Presentation - ZABTech center HyderabadMehtabRohela
Cisco ASA is a network security appliance that combines firewall, antivirus, intrusion prevention, and VPN capabilities. It provides threat defense by monitoring network traffic and can deny or permit access between internal and external networks. Key features include packet filtering, network address translation, application inspection, VPN support, and high availability options. The ASA can operate in routed or transparent firewall modes and supports authentication, dynamic routing, clustering, and next-generation firewall features like advanced malware protection. It is suitable for both small and large networks due to scalability and modular design.
EventCombMT is a highly-configurable tool that writes system events to the Windows Event Log. It is part of the Sysinternals suite and is under active development. EventCombMT is useful for security, support, and IT teams to monitor system activity through the Windows Event Log. It allows filtering of event log entries through configurable rule groups.
The document provides an overview of Splunk's machine data platform and capabilities for collecting, analyzing, and visualizing machine data from various sources. It discusses Splunk's approaches to machine data including universal indexing and schema-on-the-fly. It also covers Splunk's portfolio including apps, add-ons, and premium solutions. Finally, it discusses various methods for collecting non-traditional data sources such as network inputs, HTTP Event Collector, log event alerts, Splunk Stream, scripted inputs, database inputs, and modular inputs.
This document provides an overview and agenda for a Splunk Machine Data Workshop 101 session on data enrichment techniques in Splunk including tags, field aliases, calculated fields, and lookups. It discusses how these features add context and meaning to raw machine data by labeling, normalizing, and augmenting data. Examples are given of creating and applying each enrichment method and searching events with the enriched fields.
Who should attend? Beginner - New to Splunk and have not used it before.
Description: Machine-generated data is one of the fastest growing and complex areas of big data. It's also one of the most valuable, containing a definitive record of all user transactions, customer behavior, machine behavior, security threats, fraudulent activity and more. Join us as we explore the basics of machine data analysis and highlight techniques to help you turn your organization’s machine data into valuable insights. This introductory workshop includes a hands-on(bring your laptop) demonstration of Splunk’s technology and covers use cases both inside and outside IT. Learn why more than 13,000 customers in over 110 countries use Splunk to make business, government, and education more efficient, secure, and profitable.
Machine-generated data is one of the fastest growing and complex areas of big data. It's also one of the most valuable, containing a definitive record of all user transactions, customer behavior, machine behavior, security threats, fraudulent activity and more. Join us as we explore the basics of machine data analysis and highlight techniques to help you turn your organization’s machine data into valuable insights. This introductory workshop includes a hands-on(bring your laptop) demonstration of Splunk’s technology and covers use cases both inside and outside IT. Learn why more than 12,000 customers in over 110 countries use Splunk to make business, government, and education more efficient, secure, and profitable.
Machine Data 101: Turning Data Into Insight is a presentation about using Splunk software to analyze machine data. It discusses topics such as:
- What machine data is and examples of common sources like log files, social media, call center systems
- How Splunk indexes machine data from various sources in real-time regardless of format
- Techniques for enriching data in Splunk like tags, field aliases, calculated fields, event types, and lookups from external data sources
- Examples of collecting non-traditional data sources into Splunk like network data, HTTP events, databases, and mobile app data
The presentation provides an overview of Splunk's machine data platform and techniques for analyzing, enrich
This document provides an overview of Splunk Enterprise, including what it is, how it deploys and integrates, and its capabilities around real-time search, alerting, and reporting. Splunk Enterprise is an industry-leading platform for machine data that allows users to search, monitor, and analyze machine data from any source, location, or volume in real-time or historically. It deploys easily in 4 steps and scales to handle hundreds of terabytes of data per day from diverse sources like servers, applications, sensors, and more.
Splunk is an industry-leading platform for machine data that allows users to access, analyze, and take action on data from any source. It uses universal indexing to ingest data in real-time from various sources without needing predefined schemas. This enables search, reporting, and alerting across all machine data. Splunk can scale to handle large volumes and varieties of data, provides a developer platform for customization, and supports both on-premises and cloud deployments.
Delivering New Visibility and Analytics for IT OperationsGabrielle Knowles
The document discusses how Splunk provides visibility and analytics for IT operations. It outlines Splunk's ability to ingest data from various sources like applications, databases, networks and more. This gives organizations a universal platform to gain operational visibility, enable proactive monitoring, and obtain business insights from their machine data in real-time. Splunk differentiators include analyzing all data, scaling for large environments, and reducing MTTR, costs and improving user experiences.
The document discusses how Splunk provides visibility and analytics for IT operations. It describes how Splunk can ingest data from various sources like applications, databases, networks, virtualization and more. This gives organizations operational visibility across their infrastructure and enables proactive monitoring, search and investigation capabilities for troubleshooting and problem solving. Splunk offers a universal platform for machine data that can scale to handle large, complex environments.
The document discusses how Splunk provides visibility and analytics for IT operations. It outlines Splunk's ability to ingest data from various sources like applications, databases, networks and more. This gives organizations a universal platform to gain operational visibility, enable proactive monitoring, and power search and investigation across machine data for improved IT operations and business insights.
This document provides an overview and agenda for a Machine Data 101 presentation. The presentation covers Splunk fundamentals including the Splunk architecture and components, data sources both traditional and non-traditional, data enrichment techniques including tags, field aliases, calculated fields, event types, and lookups. Labs are included to help attendees get hands-on experience with indexing sample data, performing data discovery, and enriching data.
Splunk Data Onboarding Overview - Splunk Data Collection ArchitectureSplunk
Splunk's Naman Joshi and Jon Harris presented the Splunk Data Onboarding overview at SplunkLive! Sydney. This presentation covers:
1. Splunk Data Collection Architecture 2. Apps and Technology Add-ons
3. Demos / Examples
4. Best Practices
5. Resources and Q&A
Reactive to Proactive: Intelligent Troubleshooting and Monitoring with SplunkSplunk
This document outlines an agenda and presentation for a Splunk workshop on reactive to proactive troubleshooting and monitoring. The agenda includes an introduction to Splunk for IT operations, hands-on IT operations exercises, an overview of relevant Splunk apps, an introduction to Splunk IT Service Intelligence, and customer stories. The presentation discusses how Splunk can help transform IT from reactive problem solving to proactive monitoring and operational intelligence. It highlights key Splunk capabilities like searching, monitoring, alerting and visualizing machine data from various sources to improve troubleshooting, uptime, and IT productivity. [/SUMMARY]
SplunkLive! Frankfurt 2018 - Data Onboarding OverviewSplunk
Presented at SplunkLive! Frankfurt 2018:
Splunk Data Collection Architecture
Apps and Technology Add-ons
Demos / Examples
Best Practices
Resources and Q&A
inmation Software GmbH, located near Cologne, Germany, is a specialized software vendor in the area of system integration and industrial IT. inmation offers a software platform - system:inmation - which is a horizontally scalable, distributed information management system for production data, or any time-related information, entirely based on recent software technologies. In addition, inmation and its international partner network act as a competent team to help manufacturing industries embarking on 360° system integration and complete Enterprise Control to achieve their goals in an efficient and sustained manner.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
Machine Data Is EVERYWHERE: Use It for TestingTechWell
As more applications are hosted on servers, they produce immense quantities of logging data. Quality engineers should verify that apps are producing log data that is existent, correct, consumable, and complete. Otherwise, apps in production are not easily monitored, have issues that are difficult to detect, and cannot be corrected quickly. Tom Chavez presents the four steps that quality engineers should include in every test plan for apps that produce log output or other machine data. First, test that the data is being created. Second, ensure that the entries are correctly formatted and complete. Third, make sure the data can be consumed by your company’s log analysis tools. And fourth, verify that the app will create all possible log entries from the test data that is supplied. Join Tom as he presents demos including free tools. Learn the steps you need to include in your test plans so your team’s apps not only function but also can be monitored and understood from their machine data when running in production.
Splunk for IT Operations Breakout SessionGeorg Knon
This document discusses how IT complexity is a challenge for CIOs due to siloed technologies, disconnected point solutions, and time spent maintaining rather than innovating. It presents Splunk as a solution that provides comprehensive visibility across infrastructure, applications, databases, and more through centralized data collection and analysis. Splunk reduces problem resolution time by 67% and escalations by 90% by enabling "first responders" to search across all IT data from a single interface. The document also outlines how Splunk apps can provide insights by role and technology and its capabilities for various IT functions like virtualization, storage, and operating systems.
This document discusses how IT complexity is a challenge for CIOs due to siloed technologies, disconnected point solutions, and time spent maintaining rather than innovating. It presents Splunk as a solution that provides comprehensive visibility across infrastructure, applications, and customer-facing data to help reduce problem resolution time and escalations. Specific benefits highlighted include consolidating logs from various systems into a single view, enabling faster identification and resolution of issues. The document also outlines how Splunk apps can provide insights by role and accelerate value through plug-ins and templates to analyze data from any source.
Similar to Machine Data 101: Turning Data Into Insight (20)
.conf Go 2023 - Raiffeisen Bank InternationalSplunk
This document discusses standardizing security operations procedures (SOPs) to increase efficiency and automation. It recommends storing SOPs in a code repository for versioning and referencing them in workbooks which are lists of standard tasks to follow for investigations. The goal is to have investigation playbooks in the security orchestration, automation and response (SOAR) tool perform the predefined investigation steps from the workbooks to automate incident response. This helps analysts automate faster without wasting time by having standard, vendor-agnostic procedures.
.conf Go 2023 - Das passende Rezept für die digitale (Security) Revolution zu...Splunk
.conf Go 2023 presentation:
"Das passende Rezept für die digitale (Security) Revolution zur Telematik Infrastruktur 2.0 im Gesundheitswesen?"
Speaker: Stefan Stein -
Teamleiter CERT | gematik GmbH M.Eng. IT-Sicherheit & Forensik,
doctorate student at TH Brandenburg & Universität Dresden
El documento describe la transición de Cellnex de un Centro de Operaciones de Seguridad (SOC) a un Equipo de Respuesta a Incidentes de Seguridad (CSIRT). La transición se debió al crecimiento de Cellnex y la necesidad de automatizar procesos y tareas para mejorar la eficiencia. Cellnex implementó Splunk SIEM y SOAR para automatizar la creación, remediación y cierre de incidentes. Esto permitió al personal concentrarse en tareas estratégicas y mejorar KPIs como tiempos de resolución y correos electrónicos anal
conf go 2023 - El camino hacia la ciberseguridad (ABANCA)Splunk
Este documento resume el recorrido de ABANCA en su camino hacia la ciberseguridad con Splunk, desde la incorporación de perfiles dedicados en 2016 hasta convertirse en un centro de monitorización y respuesta con más de 1TB de ingesta diaria y 350 casos de uso alineados con MITRE ATT&CK. También describe errores cometidos y soluciones implementadas, como la normalización de fuentes y formación de operadores, y los pilares actuales como la automatización, visibilidad y alineación con MITRE ATT&CK. Por último, señala retos
Splunk - BMW connects business and IT with data driven operations SRE and O11ySplunk
BMW is defining the next level of mobility - digital interactions and technology are the backbone to continued success with its customers. Discover how an IT team is tackling the journey of business transformation at scale whilst maintaining (and showing the importance of) business and IT service availability. Learn how BMW introduced frameworks to connect business and IT, using real-time data to mitigate customer impact, as Michael and Mark share their experience in building operations for a resilient future.
The document is a presentation on cyber security trends and Splunk security products from Matthias Maier, Product Marketing Director for Security at Splunk. The presentation covers trends in security operations like the evolution of SOCs, new security roles, and data-centric security approaches. It also provides updates on Splunk's security portfolio including recognition as a leader in SIEM by Gartner and growth in the SIEM market. Maier highlights some breakout sessions from the conference on topics like asset defense, machine learning, and building detections.
Data foundations building success, at city scale – Imperial College LondonSplunk
Universities have more in common with modern cities than traditional places of learning. This mini city needs to empower its citizens to thrive and achieve their ambitions. Operationalising data is key to building critical services; from understanding complex IT estates for smarter decision-making to robust security and a more reliable, resilient student experience. Juan will share his experience in building data foundations for a resilient future whilst enabling digital transformation at Imperial College London.
Splunk: How Vodafone established Operational Analytics in a Hybrid Environmen...Splunk
Learn how Vodafone has provided end-to-end visibility across services by building an Operational Analytics Platform. In this session, you will hear how Stefan and his team manage legacy, on premise, hybrid and public cloud services, and how they are providing a platform for complex triage and debugging to tackle use cases across Vodafone’s extensive ecosystem.
.italo operates an Essential Service by connecting more than 100 million people annually across Italy with its super fast and secure railway. And CISO Enrico Maresca has been on a whirlwind journey of his own.
Formerly a Cyber Security Engineer, Enrico started at .italo as an IT Security Manager. One year later, he was promoted to CISO and tasked with building out – and significantly increasing the maturity level – of the SOC. The result was a huge step forward for .italo.
So how did he successfully achieve this ambitious ask? Join Enrico as he reveals the key insights and lessons learned in his SOC journey, including:
Top challenges faced in improving security posture
Key KPIs implemented in order to measure success
Strategies and approaches applied in the SOC
How MITRE ATT&CK and Splunk Enterprise Security were utilised
Next steps in their maturity journey ahead
This document summarizes a presentation about observability using Splunk. It includes an agenda introducing observability and why Splunk for observability. It discusses the need for modernization initiatives in companies and the thousands of changes required. It presents that Splunk provides end-to-end visibility across metrics, traces and logs to detect, troubleshoot and optimize systems. It shares a customer case study of Accenture using Splunk observability in their hybrid cloud environment. Finally, it concludes that observability with Splunk can drive results like reduced downtime and faster innovation.
This document contains slides from a Splunk presentation covering the following topics:
- Updated Splunk logo and information about meetings in Zurich and sales engineering leads
- Ideas for confused or concerned human figures in design concepts
- Three buckets of challenges around websites slowing, apps being down, and supply chain issues
- Accelerating mean time to detect, identify, respond and resolve through cyber resilience with Splunk
- Unifying security, IT and DevOps teams
- Splunk's technology vision focusing on customer experience, hybrid/edge, unleashing data lakes, and ubiquitous machine learning
- Gaining operational resilience through correlating infrastructure, security, application and user data with business outcomes
This document summarizes a presentation about Splunk's platform. It discusses Splunk's mission of helping customers create value faster with insights from their data. It provides statistics on Splunk's daily ingest and users. It highlights examples of how Splunk has helped customers in areas like internet messaging and convergent services. It also discusses upcoming challenges and new capabilities in Splunk like federated search, flexible indexing, ingest actions, improved data onboarding and management, and increased platform resilience and security.
The document appears to be a presentation from Splunk on security topics. It includes sections on cyber security resilience, the data-centric modern SOC, application monitoring at scale, threat modeling, security monitoring journeys, self-service Splunk infrastructure, the top 3 CISO priorities of risk based alerting, use case development, a security content repository, security PVP (posture, vision, and planning) and maturity assessment, and concludes with an overview of how Splunk can provide end-to-end visibility across an organization.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
The rise of big data has forced IT organizations to transition from a focus on structured, relational data, to accommodate unstructured data, driven by the volume, velocity and variety of today’s applications and systems. As the data has changed from structured data to unstructured data, the technology approach needs to change as well.
When you don’t know what data types you’ll need to analyze tomorrow or what questions you need to ask in a week, flexibility becomes a key component of your technology decisions. The ability to index any data type, search across silos and avoid being locked into a rigid schema opens a new world of analytics and business insights to your organization.
Schema at Read – Enables you ask any question of the deal
Search – Enables rapid, iterative exploration of the data along with advanced analytics
Universal Indexing – Enables you to ingest any type of machine data
Horizontal scaling over commodity hardware enables big data analytics
Splunk is the platform for machine data, it digests all machine data and allow users to quickly analyze their data and rapidly obtain insight. The platform was designed around the premise of being able to consume any machine data even if the format changes. A relational database would cannot effectively support constantly changing underlying schemas. Splunk solves this by creating a schema on the fly…
Splunk Cloud is only available in the U.S. and Canada.
The Splunk platform consists of multiple products to fit your needs.
The products can pull in data from virtually any source to support multiple use cases. And we continue to invest heavily as new sources become available.
There are 1,000s of apps built specifically to extend and simplify deployments by providing pre-packaged content designed for specific use cases and data types.
Premium apps that are built by Splunk are available for Security, IT Service Intelligence and User Behavior Analytics .
Will spend most of our time on the first 3 topics.
Splunk’s mission statement is to make machine data accessible, useful and valuable to everyone. Splunk can take any machine data and automatically index it for fast searching. Because Splunk doesn’t use a database, there are no additional licenses, and most importantly, no pre-defined schema to limit how you use your information.
Examples include the configuration files, syslog, Windows events and registry settings, as well as WMI. But the most important thing to note is how easy it is to get data into Splunk and make it useful.
The Splunk Stream software captures real-time wire data from distributed infrastructures, including private, public and hybrid clouds with on-the-fly deployment and fine-grained filtering capabilities.
Splunk DB Connect delivers reliable, scalable, real-time integration between Splunk Enterprise and traditional relational databases. With Splunk DB Connect, structured data from relational databases can be easily integrated into Splunk Enterprise, driving deeper levels of operational intelligence and richer business analytics across the organization.
Organizations can drive more meaningful insights for IT operations, security and business users. For example, IT operations teams can track performance, outage and usage by department, location and business entities. Security professionals can correlate machine data with critical assets and watch-lists for: incident investigations, real-time correlations and advanced threat detection using the award-winning Splunk Enterprise. Business users can analyze service levels and user experience by customer in real-time to make more informed decisions.
To address the needs of developers, operations and product management, you need Operational Intelligence for your mobile apps. This is what we call mobile intelligence. Mobile intelligence provides real-time insight on how your mobile apps are performing, and can correlate with and enhance Operational Intelligence.
Splunk software enables organizations to search, monitor, analyze and visualize machine-generated data from websites, applications, servers, networks, sensors and mobile devices. Splunk MINT helps organizations monitor mobile app usage and performance, gain deep visibility into mobile app transactions and accelerate development
Deliver better performing, more reliable apps
When a user has a problem with a mobile app, the issue could be isolated or spread across all app versions, handsets and OS types. With Splunk MINT, you can see issues with app performance or availability in real time. Bugs can be addressed quickly, and app developers can gain a head start in creating and delivering valuable app updates.
Achieve End-to-End visibility
When mobile apps fail, there are many potential sources of failure. With Splunk MINT, you can analyze overall transaction performance. And using Splunk MINT, you can correlate this data with information from back-end apps to gain detailed insight on transaction problems. As a result, operations can reduce MTTR and better anticipate future mobile app back-end requirements.
Deliver real-time analytics
Mobile apps give enterprises new ways of conducting digital business. With mobile app information in Splunk Enterprise, you can correlate usage and performance information— some call this omni-channel analytics—to better understand how users are engaging all aspects of your organization.
Splunk MINT Express provides a dashboard that offers and at a glance view of Mobile app health and usage. This includes an overall index called “MobDex”, which provides a blended view of Application usage, crashes, engagement in and abandonment. The insight boxes provide top-level aggregated information, which you can click on to get more specific information, and context.
Hadoop data roll is an option available to customers who would like to retain their historical Splunk data in their Hadoop data lake. This functionality used to be part of the Hunk product, but it now integrated within Splunk Enterprise and included with your license. It is compatible with most popular Apache Hadoop distributions as well as Amazon EMR running on S3 storage. The main benefit of Hadoop data roll is TCO reduction achieved by reducing the storage footprint and lower cost storage hardware. Additionally, your Hadoop applications will be able to use data that was originally indexed in Splunk.
Reduction in storage footprint is achieved by reducing Splunk search optimization data that are primarily used to speed up ”needle in the haystack” type searches. The storage footprint reductions can range from 40-80%, depending on the characteristics of the underlying data.
Once data is rolled into Hadoop, search, reporting, and analysis functionality within Splunk Enterprise is retained through virtual indexes, though with performance tradeoffs. Another consideration is that Splunk premium solutions like ES and ITSI don’t yet support use of data that has been rolled into Hadoop.
Splunk MINT Express provides a dashboard that offers and at a glance view of Mobile app health and usage. This includes an overall index called “MobDex”, which provides a blended view of Application usage, crashes, engagement in and abandonment. The insight boxes provide top-level aggregated information, which you can click on to get more specific information, and context.
The data for example may have a userid but you want to search on a name. Splunk’s lookup capability can enrich the raw data by adding additional fields at search time by. Some common use cases including event and error code description fields. Think “Page not Found” instead of “404”. Enriching your data can lead to entirely new insight.
In the example shown, Splunk took the userid and looked up the name and role of the user from an HR database. Similarly, it determined the location of the failed log in attempt by correlating the IP address. Even though these fields don’t exist in the raw data, Splunk allows you to search or pivot on them at any time.
You can also mask data. For example, you may want social security numbers to be replaced with all X’s for regular users but not masked for others. Removing data can also be useful, such as filtering PII, before writing it to an index in Splunk.
Splunk 6 takes large-scale machine data analytics to the next level by introducing three breakthrough innovations:
Pivot – opens up the power of Splunk search to non-technical users with an easy-to-use drag and drop interface to explore, manipulate and visualize data
Data Model – defines meaningful relationships in underlying machine data and making the data more useful to broader base of non-technical users
Analytics Store – patent pending technology that accelerates data models by delivering extremely high performance data retrieval for analytical operations, up to 1000x faster than Splunk 5
Let’s dig into each of these new features in more detail.
Data Models are created using the Data Model Builder and are usually designed and implemented by users who understand the format and semantics of their indexed data, and who are familiar with the Splunk Search Processing Language (SPL). They define meaningful relationships in the data.
Unlike data models in the traditional structured world, Splunk Data Models focus on machine data and data mashups between machine data and structured data. Splunk software is founded on the ability to flexibly search and analyze highly diverse machine data employing late-binding or search-time techniques for schematization (“schema-on-the-fly”). And Data Models are no exception. They define relationships in the underlying data, while leaving the raw machine data intact, and map these relationships at search time. They are therefore highly flexible and designed to enable users to rapidly iterate.
Security is also a key consideration and data models are fully permissionable in Splunk 6.
Data Models are created using the Data Model Builder and are usually designed and implemented by users who understand the format and semantics of their indexed data, and who are familiar with the Splunk Search Processing Language (SPL). They define meaningful relationships in the data.
Unlike data models in the traditional structured world, Splunk Data Models focus on machine data and data mashups between machine data and structured data. Splunk software is founded on the ability to flexibly search and analyze highly diverse machine data employing late-binding or search-time techniques for schematization (“schema-on-the-fly”). And Data Models are no exception. They define relationships in the underlying data, while leaving the raw machine data intact, and map these relationships at search time. They are therefore highly flexible and designed to enable users to rapidly iterate.
Security is also a key consideration and data models are fully permissionable in Splunk 6.
Data Models are created using the Data Model Builder and are usually designed and implemented by users who understand the format and semantics of their indexed data, and who are familiar with the Splunk Search Processing Language (SPL). They define meaningful relationships in the data.
Unlike data models in the traditional structured world, Splunk Data Models focus on machine data and data mashups between machine data and structured data. Splunk software is founded on the ability to flexibly search and analyze highly diverse machine data employing late-binding or search-time techniques for schematization (“schema-on-the-fly”). And Data Models are no exception. They define relationships in the underlying data, while leaving the raw machine data intact, and map these relationships at search time. They are therefore highly flexible and designed to enable users to rapidly iterate.
Security is also a key consideration and data models are fully permissionable in Splunk 6.
What does this platform look like?
The platform consists of 2 layer:
A core engine and an interface layer
On top of the platform you can’t run a broad spectrum of content that supports use cases
Use cases range from application mgmt. and IT operations, to ES and PCI compliance, to web analytics
The core engine provides the basic services for real time data input, indexing and search as well alerting, large scale distributed processing and role based access
The Interface layer consist of the basic UI for search, reporting and visualization – it contains developer interfaces, the REST API, SDKs and Web Framework
The SDKs provide a convenient access to core engine services in a variety of programing language environments.
The Web Framework enables developers to quickly create Splunk Apps by using the modern web programming paradigm including pre-built components, styles, templates,
and reusable samples as well as supporting the development of custom logic, interactions, components, and UI.
Developers can choose to program their Splunk App using Simple XML, JavaScript or Django (or any combination thereof).
These programmatic interfaces allow you to eithe:r:
extend Splunk
integrate Splunk with other applications
build completely new applications from scratch that require OI or analytical services that Splunk provides
What have developers been building using Splunk Enterprise? Examples include the following:
Run searches and retrieve Splunk data from existing Customer Service/Call Center applications (Comcast use case)
Integrate Splunk data into existing BI tools and dashboard (Tableau, MS Excel)
Build mobile applications with KPI dashboards and alerts powered by Splunk (Otto Group use case)
Log directly to Splunk from remote devices (Bosch use cases)
Build customer-facing dashboards powered by user-specific data in Splunk (Socialize, Hurricane Labs use cases)
Programmatically extract data from Splunk for long-term data warehousing
We hope this is just the beginning. We hope to open up a whole new world of enterprise apps.
The Splunk Web Framework Toolkit is a resource to help developers learn how to build rich applications using the Splunk Web Framework. This app contains a collection of examples that show you how to use the components of the Web Framework. You will find documentation and reference information, along with code templates and additional components that you can use within your own Splunk Apps.
BUILD SPLUNK APPS
The Splunk Web Framework makes building a Splunk app looks and feels like building any modern web application.
The Simple Dashboard Editor makes it easy to BUILD interactive dashboards and user workflows as well as add custom styling, behavior and visualizations. Simple XML is ideal for fast, lightweight app customization and building. Simple XML development requires minimal coding knowledge and is well-suited for Splunk power users in IT to get fast visualization and analytics from their machine data. Simple XML also lets the developer “escape” to HTML with one click to do more powerful customization and integration with JavaScript.
Developers looking for more advanced functionality and capabilities can build Splunk apps from the ground up using popular, standards-based web technologies: JavaScript and Django. The Splunk Web Framework lets developers quickly create Splunk apps by using prebuilt components, styles, templates, and reusable samples as well as supporting the development of custom logic, interactions, components, and UI. Developers can choose to program their Splunk app using Simple XML, JavaScript or Django (or any combination thereof).
EXTEND AND INTEGRATE SPLUNK
Splunk Enterprise is a robust, fully-integrated platform that enables developers to INTEGRATE data and functionality from Splunk software into applications across the organization using Software Development Kits (SDKs) for Java, JavaScript, C#, Python, PHP and Ruby. These SDKs make it easier to code to the open REST API that sits on top of the Splunk Engine. With almost 200 endpoints, the REST API lets developers do programmatically what any end user can do in the UI and more. The Splunk SDKs include documentation, code samples, resources and tools to make it faster and more efficient to program against the Splunk REST API using constructs and syntax familiar to developers experienced with Java, Python, JavaScript, PHP, Ruby and C#. Developers can easily manage HTTP access, authentication and namespaces in just a few lines of code.
Developers can use the Splunk SDKs to:
- Run real-time searches and retrieve Splunk data from line-of-business systems like Customer Service applications
- Integrate data and visualizations (charts, tables) from Splunk into BI tools and reporting dashboards
- Build mobile applications with real-time KPI dashboards and alerts powered by Splunk
- Log directly to Splunk from remote devices and applications via TCP, UDP and HTTP
- Build customer-facing dashboards in your applications powered by user-specific data in Splunk
- Manage a Splunk instance, including adding and removing users as well as creating data inputs from an application outside of Splunk
- Programmatically extract data from Splunk for long-term data warehousing
Developers can EXTEND the power of Splunk software with programmatic control over search commands, data sources and data enrichment.
Splunk Enterprise offers search extensibility through:
- Custom Search Commands - developers can add a custom search script (in Python) to Splunk to create own search commands. To build a search that runs recursively, developers need to make calls directly to the REST API
- Scripted Lookups: developers can programmatically script lookups via Python.
- Scripted Alerts: can trigger a shell script or batch file (we provide guidance for Python and PERL).
- Search Macros: make chunks of a search reuseable in multiple places, including saved and ad hoc searches.
Splunk also provides developers with other mechanisms to extend the power of the platform.
- Data Models: allow developers to abstract away the search language syntax, making Splunk queries (and thus, functionality) more manageable and portable/shareable.
- Modular Inputs: allow developers to extend Splunk to programmatically manage custom data input functionality via REST.
Splunk Enterprise empowers developers with application intelligence across the entire product development lifecycle, from monitoring code check-ins and build servers, to pinpointing production issues in real-time and gaining valuable insights on application usage and user preferences. Splunk Enterprise is a robust platform that enables developers to integrate data and functionality from Splunk software into applications across the organization using Software Development Kits (SDKs) for Java, JavaScript, C#, Python, PHP and Ruby. Developers can extend the power of Splunk software with programmatic control over search commands, data sources and data enrichment. Developers can use the tools and languages they know to build Splunk apps with custom dashboards, flexible UI and custom data visualizations, using the Splunk Web Framework.
Here are just some of the new Splunk Apps that have been delivered over the past year.
Their goal is to make it easier to use Splunk for specific technologies and use cases – prepackaging inputs, field extractions, searches and visualizations.
Highlight a few apps.
These apps along with 100’s of others have been developed not only by Splunk but by partners, customers and members of the Splunk community.
“After this workshop, if you want more information, all the product documentation is available online. The documentation is divided into several manuals. For reporting and dashboards you will likely be most interested in the User and Developer Manuals.”
“For a more interactive approach to getting your questions addressed there is Splunk Answers. It is a web based Splunk community of Splunkers like you. Splunk employees are also regular experts on the site.”
“It is not possible to cover everything you need to know about building reports and dashboards in 30-45 minutes. For more structured training with labs, consider Splunk education courses. These are available as instructor-led web-based courses or onsite if there is enough participants per class.”
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Course Topics
Overview of ITSI features
ITSI architecture and deployment
Installing ITSI
Designing and implementing services and entities
Configuring correlation searches and notable events
Creating deep dive pages
Creating glass tables
ITSI troubleshooting
Course Topics
Overview of ITSI features
ITSI architecture and deployment
Installing ITSI
Designing and implementing services and entities
Configuring correlation searches and notable events
Creating deep dive pages
Creating glass tables
ITSI troubleshooting
Are you looking to learn, share, and participate with other Splunk users? Visit usergroups.splunk.com, search for <<City Name>>, and join the local user group to receive updates on upcoming meetings!