This document summarizes a webinar presented by Leon Guzenda of Objectivity, Inc. on choosing the right big data tools. It discusses how current big data analytics use separate technologies that are not well-suited for relationship analytics. A polyglot approach is recommended using the appropriate technologies like object databases and graph databases to efficiently store, manage and query relationships in complex data. Objectivity provides the InfiniteGraph massively scalable graph database and Objectivity/DB object database for managing interconnected data and hidden relationships.
Trusted advisory on technology comparison --exadata, hana, db2Ajay Kumar Uppal
- SAP HANA is a column-oriented, in-memory database that promises performance gains of up to 100,000x over traditional databases and enables new real-time use cases. Its appliance model reduces costs by simplifying infrastructure requirements. However, it requires new extreme main memory hardware and has limitations for high availability, disaster recovery, and virtualization initially.
- Oracle Exadata is an optimized hardware and software appliance for Oracle Database that scales to hundreds of terabytes. It provides fast performance through SSD caching and compression but does not have a true column-oriented architecture. Additional products like TimesTen and Essbase are needed for optimal OLAP support.
- IBM DB2 with BLU extension provides query acceleration for OL
Hadoop World 2011: Unlocking the Value of Big Data with Oracle - Jean-Pierre ...Cloudera, Inc.
Analyzing new and diverse digital data streams can reveal new sources of economic value, provide fresh insights into customer behavior and identify market trends early on. But this influx of new data can create challenges for IT departments. To derive real business value from Big Data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Attend this session to learn how Oracle’s end-to-end value chain for Big Data can help you unlock the value of Big Data.
If you are seeking ways to improve your cloud database environment with EDB Postgres, this presentation reviews how you can create a Database-as-a-Service (DBaaS) with EDB Postgres on AWS.
This presentation outlines how EDB Ark can play a key role in your digital transformation with more agility and speed.
It highlights:
● How EDB Ark can integrate with your existing AWS environment and other clouds
● How you can automate your database deployments to instantly spin up new databases
● How to manage your database environment easier using the same GUI for all clouds
● How to boost developer efficiency and satisfaction
Whether your database is currently in the cloud or you are considering the cloud as an option, this presentation will provide you with the information you need to evaluate EDB Postgres and EDB Ark.
The recording of this presentation includes a demonstration. Visit www.edbpostgres.com > resources > webcasts
The Real Scoop on Migrating from Oracle DatabasesEDB
During this presentation you will be provided with actionable guidelines to:
• Identify the right applications to migrate
• Easily and safely migrate your applications
• Leverage resources before, during and after your migration
• Learn how to achieve independence from Oracle databases - without sacrificing performance.
Operationalizing Data Science Using Cloud FoundryVMware Tanzu
The document discusses how operationalizing machine learning models through continuous deployment and monitoring is important to realize business value but often overlooked, and describes how Alpine Data's Chorus platform in combination with Pivotal's Big Data Suite and Cloud Foundry can provide a turn-key solution for operationalizing models by deploying scalable scoring engines that can consume models exported in the PFA format. The platform aims to make it simple to deploy both individual models and complex scoring flows represented as PFA documents to ensure models have maximum impact on the business.
Postgres Integrates Effectively in the "Enterprise Sandbox"EDB
This presentation provides guidance through these challenges and provide solutions that allow you to:
- Connect to multiple sources of data to support your growing business
- Integrate with existing incumbent systems that power your business
- Share siloed data among your technical teams to address strategic objectives
- Learn how customers integrated EDB Postgres within their corporate ecosystems that included Oracle, SQL Server, MongoDB, Hadoop, MySQL and Tuxedo
This presentation covers the solutions, services, and best practice recommendations you need to be a leader in today’s complex digital environment.
Target Audience: The content will interest both business and technical decision-makers or influencers responsible for the overall strategy and execution of a PostgreSQL and/or an EDB Postgres database.
Ashnik EnterpriseDB PostgreSQL - A real alternative to Oracle Ashnikbiz
A Technical introduction to PostgreSQL and Postgres Plus -
Enterprise Class PostgreSQL Database from EDB - You have a ‘Real’ alternative to Oracle and other conventional proprietary Databases
Trusted advisory on technology comparison --exadata, hana, db2Ajay Kumar Uppal
- SAP HANA is a column-oriented, in-memory database that promises performance gains of up to 100,000x over traditional databases and enables new real-time use cases. Its appliance model reduces costs by simplifying infrastructure requirements. However, it requires new extreme main memory hardware and has limitations for high availability, disaster recovery, and virtualization initially.
- Oracle Exadata is an optimized hardware and software appliance for Oracle Database that scales to hundreds of terabytes. It provides fast performance through SSD caching and compression but does not have a true column-oriented architecture. Additional products like TimesTen and Essbase are needed for optimal OLAP support.
- IBM DB2 with BLU extension provides query acceleration for OL
Hadoop World 2011: Unlocking the Value of Big Data with Oracle - Jean-Pierre ...Cloudera, Inc.
Analyzing new and diverse digital data streams can reveal new sources of economic value, provide fresh insights into customer behavior and identify market trends early on. But this influx of new data can create challenges for IT departments. To derive real business value from Big Data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Attend this session to learn how Oracle’s end-to-end value chain for Big Data can help you unlock the value of Big Data.
If you are seeking ways to improve your cloud database environment with EDB Postgres, this presentation reviews how you can create a Database-as-a-Service (DBaaS) with EDB Postgres on AWS.
This presentation outlines how EDB Ark can play a key role in your digital transformation with more agility and speed.
It highlights:
● How EDB Ark can integrate with your existing AWS environment and other clouds
● How you can automate your database deployments to instantly spin up new databases
● How to manage your database environment easier using the same GUI for all clouds
● How to boost developer efficiency and satisfaction
Whether your database is currently in the cloud or you are considering the cloud as an option, this presentation will provide you with the information you need to evaluate EDB Postgres and EDB Ark.
The recording of this presentation includes a demonstration. Visit www.edbpostgres.com > resources > webcasts
The Real Scoop on Migrating from Oracle DatabasesEDB
During this presentation you will be provided with actionable guidelines to:
• Identify the right applications to migrate
• Easily and safely migrate your applications
• Leverage resources before, during and after your migration
• Learn how to achieve independence from Oracle databases - without sacrificing performance.
Operationalizing Data Science Using Cloud FoundryVMware Tanzu
The document discusses how operationalizing machine learning models through continuous deployment and monitoring is important to realize business value but often overlooked, and describes how Alpine Data's Chorus platform in combination with Pivotal's Big Data Suite and Cloud Foundry can provide a turn-key solution for operationalizing models by deploying scalable scoring engines that can consume models exported in the PFA format. The platform aims to make it simple to deploy both individual models and complex scoring flows represented as PFA documents to ensure models have maximum impact on the business.
Postgres Integrates Effectively in the "Enterprise Sandbox"EDB
This presentation provides guidance through these challenges and provide solutions that allow you to:
- Connect to multiple sources of data to support your growing business
- Integrate with existing incumbent systems that power your business
- Share siloed data among your technical teams to address strategic objectives
- Learn how customers integrated EDB Postgres within their corporate ecosystems that included Oracle, SQL Server, MongoDB, Hadoop, MySQL and Tuxedo
This presentation covers the solutions, services, and best practice recommendations you need to be a leader in today’s complex digital environment.
Target Audience: The content will interest both business and technical decision-makers or influencers responsible for the overall strategy and execution of a PostgreSQL and/or an EDB Postgres database.
Ashnik EnterpriseDB PostgreSQL - A real alternative to Oracle Ashnikbiz
A Technical introduction to PostgreSQL and Postgres Plus -
Enterprise Class PostgreSQL Database from EDB - You have a ‘Real’ alternative to Oracle and other conventional proprietary Databases
A Survey of Advanced Non-relational Database Systems: Approaches and Applicat...Qian Lin
This document summarizes a survey of advanced non-relational database systems, their approaches, applications, and comparison to relational database management systems (RDBMS). It outlines the problem of scaling to meet new web-scale demands, describes how non-relational databases provide a solution by sacrificing consistency for availability and partition tolerance. Examples of non-relational databases are provided, including their data models, APIs, optimizations, and benefits compared to RDBMS such as improved scalability and fault tolerance.
Temporal Tables, Transparent Archiving in DB2 for z/OS and IDAACuneyt Goksu
The document discusses several data archiving solutions for z/OS systems including temporal tables, transparent archiving, and IDAA technology. Temporal tables allow querying and updating historical data using system time periods. Transparent archiving moves old data to other storage platforms while still allowing dynamic queries. IDAA provides accelerated query performance for temporal tables by routing queries to an accelerator system. The solutions can be combined for different use cases depending on data retention and access needs.
This presentation reviews the key methodologies that all the member of the team should consider such as:
- How to prioritize the right application or project for your first Oracle
- Tips to execute a well-defined, phased migration process to minimize risk and increase time to value
- Handling the common concerns and pitfalls related to a migration project
- What resources you can leverage before, during and after your migration
- Suggestions on how you can achieve independence from an Oracle database – without sacrificing performance.
Target audience: This presentation is intended for IT Decision-Makers and Leaders on the team involved in Database decisions and execution.
For more information, please email sales@enterprisedb.com
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
Big Data, Simple and Fast: Addressing the Shortcomings of HadoopHazelcast
In this webinar
This talk identifies several shortcomings of Apache Hadoop and presents an alternative approach for building simple and flexible Big Data software stacks quickly, based on next generation computing paradigms, such as in-memory data/compute grids. The focus of the talk is on software architectures, but several code examples using Hazelcast will be provided to illustrate the concepts discussed.
We’ll cover these topics:
-Briefly explain why Hadoop is not a universal, or inexpensive, Big Data solution – despite the hype
-Lay out technical requirements for a flexible Big/Fast Data processing stack
-Present solutions thought to be alternatives to Hadoop
-Argue why In-Memory Data/Compute Grids are so attractive in creating future-proof Big/Fast Data applications
-Discuss how well Hazelcast meets the Big/Fast Data requirements vs Hadoop
-Present several code examples using Java and Hazelcast to illustrate concepts discussed
-Live Q&A Session
Presenter:
Jacek Kruszelnicki, President of Numatica Corporation
EDBT 2013 - Near Realtime Analytics with IBM DB2 Analytics AcceleratorDaniel Martin
The document discusses IBM's DB2 Analytics Accelerator (IDAA) which uses incremental updates to synchronize data between DB2 and the IDAA appliance in near real-time. It describes the architecture of using log-based capture and propagation to minimize latency. The user interface allows controlling replication at the subsystem and table level. High availability is ensured through failover capabilities. Tuning options and evaluation of query impact are also covered.
According to Gartner, organizations can reduce their database spend by up to 80% by deploying EDB Postgres in place of traditional database solutions like Oracle. Nevertheless, the perceived risks associated with migrating from Oracle to an open source-based alternative prevents many organizations from trying.
Review this presentation to learn some of EDB Postgres Enterprise’s more important features and techniques employed to reduce migration risk.
This presentation will be valuable to organizations researching Postgres, as well as current Oracle customers considering migrating to an open source-based database management system such as EDB Postgres. It highlights key points for both business and technical decision-makers and influencers.
Evaluating scenarios without commercial assistance, a consulting partner and EDB Postgres Standard
Are you asking yourself which Postgres solution will give you what you need?
If you are unsure, which Postgres is right for you, rest assured that others have faced the same challenge. Based on our work with other Postgres users we have developed a guide providing you with the pros and cons of various Postgres solutions so that you can make an educated decision.
This presentation introduces the usage profiles and the technical and business risks involved running PostgreSQL without commercial support, developing a fork, working with a consulting partner and subscribing to EDB Postgres.
Things Every Oracle DBA Needs to Know About the Hadoop Ecosystem 20170527Zohar Elkayam
Big data is one of the biggest buzzwords in today's market. Terms such as Hadoop, HDFS, YARN, Sqoop, and non-structured data have been scaring DBAs since 2010, but where does the DBA team really fit in?
In this session, we will discuss everything database administrators and database developers need to know about big data. We will demystify the Hadoop ecosystem and explore the different components. We will learn how HDFS and MapReduce are changing the data world and where traditional databases fit into the grand scheme of things. We will also talk about why DBAs are the perfect candidates to transition into big data and Hadoop professionals and experts.
This is the presentation I gave in Kscope17, on June 27, 2017.
During this presentation, Craig Silviera, WW Director of Field Engineering described the steps to take to dramatically reduce your IT infrastructure costs when you make the switch from Oracle.
Craig provided actionable steps to:
• Identify the right applications to migrate
• Easily and safely migrate your applications
• Leverage resources before, during and after your migration
To learn more about migrating your database from Oracle to Postgres,
please email info@enterprisedb.com and someone will follow up with you asap.
Active/Active Database Solutions with Log Based Replication in xDB 6.0EDB
EDB’s xDB Replication Server is a highly flexible database replication tool that provides single and multi-master solutions for read/write scalability, availability, performance, and data integration with Oracle, SQL Server and Postgres. Dozens of worldwide customers have been using xDB Replication Server for the past 4 years, and we are extremely excited to introduce a pivotal new release, version 6.0.
This presentation reviews the features in xDB 6.0 including:
* Faster and more efficient replication with log-based Multi Master replication for Postgres Plus and PostgreSQL
* Easier to configure publication tables in bulk with pattern matching selection rules
* Ensure High Availability with integration of the 'Control Schema'
* Improved performance in conflict detection rules
An Expert Guide to Migrating Legacy Databases to PostgreSQLEDB
his webinar will review the challenges teams face when migrating from Oracle databases to PostgreSQL. We will share insights gained from running large scale Oracle compatibility assessments over the last two years, including the over 2,200,000 Oracle DDL constructs that were assessed through EDB’s Migration Portal in 2020.
During this session we will address:
Storage definitions
Packages
Stored procedures
PL/SQL code
Proprietary database APIs
Large scale data migrations
We will end the session demonstrating migration tools that significantly simplify and aid in reducing the risk of migrating Oracle databases to PostgreSQL.
Overview of EnterpriseDB Postgres Plus Advanced Server 9.4 and Postgres Enter...EDB
The presentation will provide you with a full overview of the new features and key benefits of EnterpriseDB's Postgres Plus Advanced Server 9.4 and Postgres Enterprise Manager 5.0.
Kudu is an open source storage layer developed by Cloudera that provides low latency queries on large datasets. It uses a columnar storage format for fast scans and an embedded B-tree index for fast random access. Kudu tables are partitioned into tablets that are distributed and replicated across a cluster. The Raft consensus algorithm ensures consistency during replication. Kudu is suitable for applications requiring real-time analytics on streaming data and time-series queries across large datasets.
Minimize Headaches with Your Postgres DeploymentEDB
Postgres deployments are not difficult to put in place, but both existing and new users are wise to ask questions general Postgres questions, as well as questions specific to their implementation to ensure a successful deployment.
This presentation will help you minimize deployment challenges to ensure that your Postgres plans meet and exceed their goals.
This presentation covers the following topics:
- Identify specific key challenges that can hinder your deployment
- Overcome barriers to avoid poor results
- Discover what success can look like for your organization
- Find out how to get started with your deployment with EDB
Target Audience: This presentation is intended for Business and Technical Management overseeing a Postgres deployment team. This presentation is equally suitable for organizations already using community PostgreSQL as well as EDB’s Postgres Plus product family.
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
Business Intelligence (BI) solutions need to move at the speed of business. Unfortunately, roadblocks related to availability of resources and deployment often present an issue. What if you could accelerate the deployment of an entire BI infrastructure to just a couple hours and start loading data into it by the end of the day. In this session, we'll demonstrate how to leverage Microsoft tools and the Azure cloud environment to build out a BI solution and begin providing analytics to your team with tools such as Power BI. By end of the session, you'll gain an understanding of the capabilities of Azure and how you can start building an end to end BI proof-of-concept today.
This document provides an introduction to relational databases, NoSQL databases, and data in general. It includes the following:
- An overview of relational databases and their ACID properties. Relational databases are best for structured, centralized data and scale vertically.
- A survey of several popular NoSQL databases like MongoDB, Cassandra, Redis, and HBase. NoSQL databases are best for unstructured, large quantities of data and scale horizontally.
- General advice that the data and query models, durability needs, scalability needs, and consistency requirements should determine the best database choice. Trying different options is recommended.
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021Sandesh Rao
The document discusses Oracle Machine Learning (OML) services on Oracle Autonomous Database. It provides an overview of the OML services REST API, which allows storing and deploying machine learning models. It enables scoring of models using REST endpoints for application integration. The API supports classification/regression of ONNX models from libraries like Scikit-learn and TensorFlow. It also provides cognitive text capabilities like topic discovery, keywords, sentiment analysis and text summarization.
Oracle NoSQL DB & InfiniteGraph - Trends in Big Data and Graph TechnologyInfiniteGraph
Join Oracle NoSQL DB and InfiniteGraph development teams in a discussion of the latest trends in Big Data and Graph Technology. Learn what Oracle’s view of Big Data is and how Oracle NoSQL Database technologies enable you to manage vast amounts of real-time key-value data.
Join Objectivity, Inc.’s VP of Product Management, Brian Clark, in a discussion of the latest trends in Big Data Analytics, defining what is Big Data and understanding how to maximize your existing architectures by utilizing NOSQL technologies to improve functionality and provide real-time results. There will be a focus on relationship analytics as well as an introduction to NOSQL data stores, object and graph databases, such as the architecture behind Objectivity/DB and InfiniteGraph.
A Survey of Advanced Non-relational Database Systems: Approaches and Applicat...Qian Lin
This document summarizes a survey of advanced non-relational database systems, their approaches, applications, and comparison to relational database management systems (RDBMS). It outlines the problem of scaling to meet new web-scale demands, describes how non-relational databases provide a solution by sacrificing consistency for availability and partition tolerance. Examples of non-relational databases are provided, including their data models, APIs, optimizations, and benefits compared to RDBMS such as improved scalability and fault tolerance.
Temporal Tables, Transparent Archiving in DB2 for z/OS and IDAACuneyt Goksu
The document discusses several data archiving solutions for z/OS systems including temporal tables, transparent archiving, and IDAA technology. Temporal tables allow querying and updating historical data using system time periods. Transparent archiving moves old data to other storage platforms while still allowing dynamic queries. IDAA provides accelerated query performance for temporal tables by routing queries to an accelerator system. The solutions can be combined for different use cases depending on data retention and access needs.
This presentation reviews the key methodologies that all the member of the team should consider such as:
- How to prioritize the right application or project for your first Oracle
- Tips to execute a well-defined, phased migration process to minimize risk and increase time to value
- Handling the common concerns and pitfalls related to a migration project
- What resources you can leverage before, during and after your migration
- Suggestions on how you can achieve independence from an Oracle database – without sacrificing performance.
Target audience: This presentation is intended for IT Decision-Makers and Leaders on the team involved in Database decisions and execution.
For more information, please email sales@enterprisedb.com
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
Big Data, Simple and Fast: Addressing the Shortcomings of HadoopHazelcast
In this webinar
This talk identifies several shortcomings of Apache Hadoop and presents an alternative approach for building simple and flexible Big Data software stacks quickly, based on next generation computing paradigms, such as in-memory data/compute grids. The focus of the talk is on software architectures, but several code examples using Hazelcast will be provided to illustrate the concepts discussed.
We’ll cover these topics:
-Briefly explain why Hadoop is not a universal, or inexpensive, Big Data solution – despite the hype
-Lay out technical requirements for a flexible Big/Fast Data processing stack
-Present solutions thought to be alternatives to Hadoop
-Argue why In-Memory Data/Compute Grids are so attractive in creating future-proof Big/Fast Data applications
-Discuss how well Hazelcast meets the Big/Fast Data requirements vs Hadoop
-Present several code examples using Java and Hazelcast to illustrate concepts discussed
-Live Q&A Session
Presenter:
Jacek Kruszelnicki, President of Numatica Corporation
EDBT 2013 - Near Realtime Analytics with IBM DB2 Analytics AcceleratorDaniel Martin
The document discusses IBM's DB2 Analytics Accelerator (IDAA) which uses incremental updates to synchronize data between DB2 and the IDAA appliance in near real-time. It describes the architecture of using log-based capture and propagation to minimize latency. The user interface allows controlling replication at the subsystem and table level. High availability is ensured through failover capabilities. Tuning options and evaluation of query impact are also covered.
According to Gartner, organizations can reduce their database spend by up to 80% by deploying EDB Postgres in place of traditional database solutions like Oracle. Nevertheless, the perceived risks associated with migrating from Oracle to an open source-based alternative prevents many organizations from trying.
Review this presentation to learn some of EDB Postgres Enterprise’s more important features and techniques employed to reduce migration risk.
This presentation will be valuable to organizations researching Postgres, as well as current Oracle customers considering migrating to an open source-based database management system such as EDB Postgres. It highlights key points for both business and technical decision-makers and influencers.
Evaluating scenarios without commercial assistance, a consulting partner and EDB Postgres Standard
Are you asking yourself which Postgres solution will give you what you need?
If you are unsure, which Postgres is right for you, rest assured that others have faced the same challenge. Based on our work with other Postgres users we have developed a guide providing you with the pros and cons of various Postgres solutions so that you can make an educated decision.
This presentation introduces the usage profiles and the technical and business risks involved running PostgreSQL without commercial support, developing a fork, working with a consulting partner and subscribing to EDB Postgres.
Things Every Oracle DBA Needs to Know About the Hadoop Ecosystem 20170527Zohar Elkayam
Big data is one of the biggest buzzwords in today's market. Terms such as Hadoop, HDFS, YARN, Sqoop, and non-structured data have been scaring DBAs since 2010, but where does the DBA team really fit in?
In this session, we will discuss everything database administrators and database developers need to know about big data. We will demystify the Hadoop ecosystem and explore the different components. We will learn how HDFS and MapReduce are changing the data world and where traditional databases fit into the grand scheme of things. We will also talk about why DBAs are the perfect candidates to transition into big data and Hadoop professionals and experts.
This is the presentation I gave in Kscope17, on June 27, 2017.
During this presentation, Craig Silviera, WW Director of Field Engineering described the steps to take to dramatically reduce your IT infrastructure costs when you make the switch from Oracle.
Craig provided actionable steps to:
• Identify the right applications to migrate
• Easily and safely migrate your applications
• Leverage resources before, during and after your migration
To learn more about migrating your database from Oracle to Postgres,
please email info@enterprisedb.com and someone will follow up with you asap.
Active/Active Database Solutions with Log Based Replication in xDB 6.0EDB
EDB’s xDB Replication Server is a highly flexible database replication tool that provides single and multi-master solutions for read/write scalability, availability, performance, and data integration with Oracle, SQL Server and Postgres. Dozens of worldwide customers have been using xDB Replication Server for the past 4 years, and we are extremely excited to introduce a pivotal new release, version 6.0.
This presentation reviews the features in xDB 6.0 including:
* Faster and more efficient replication with log-based Multi Master replication for Postgres Plus and PostgreSQL
* Easier to configure publication tables in bulk with pattern matching selection rules
* Ensure High Availability with integration of the 'Control Schema'
* Improved performance in conflict detection rules
An Expert Guide to Migrating Legacy Databases to PostgreSQLEDB
his webinar will review the challenges teams face when migrating from Oracle databases to PostgreSQL. We will share insights gained from running large scale Oracle compatibility assessments over the last two years, including the over 2,200,000 Oracle DDL constructs that were assessed through EDB’s Migration Portal in 2020.
During this session we will address:
Storage definitions
Packages
Stored procedures
PL/SQL code
Proprietary database APIs
Large scale data migrations
We will end the session demonstrating migration tools that significantly simplify and aid in reducing the risk of migrating Oracle databases to PostgreSQL.
Overview of EnterpriseDB Postgres Plus Advanced Server 9.4 and Postgres Enter...EDB
The presentation will provide you with a full overview of the new features and key benefits of EnterpriseDB's Postgres Plus Advanced Server 9.4 and Postgres Enterprise Manager 5.0.
Kudu is an open source storage layer developed by Cloudera that provides low latency queries on large datasets. It uses a columnar storage format for fast scans and an embedded B-tree index for fast random access. Kudu tables are partitioned into tablets that are distributed and replicated across a cluster. The Raft consensus algorithm ensures consistency during replication. Kudu is suitable for applications requiring real-time analytics on streaming data and time-series queries across large datasets.
Minimize Headaches with Your Postgres DeploymentEDB
Postgres deployments are not difficult to put in place, but both existing and new users are wise to ask questions general Postgres questions, as well as questions specific to their implementation to ensure a successful deployment.
This presentation will help you minimize deployment challenges to ensure that your Postgres plans meet and exceed their goals.
This presentation covers the following topics:
- Identify specific key challenges that can hinder your deployment
- Overcome barriers to avoid poor results
- Discover what success can look like for your organization
- Find out how to get started with your deployment with EDB
Target Audience: This presentation is intended for Business and Technical Management overseeing a Postgres deployment team. This presentation is equally suitable for organizations already using community PostgreSQL as well as EDB’s Postgres Plus product family.
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
Business Intelligence (BI) solutions need to move at the speed of business. Unfortunately, roadblocks related to availability of resources and deployment often present an issue. What if you could accelerate the deployment of an entire BI infrastructure to just a couple hours and start loading data into it by the end of the day. In this session, we'll demonstrate how to leverage Microsoft tools and the Azure cloud environment to build out a BI solution and begin providing analytics to your team with tools such as Power BI. By end of the session, you'll gain an understanding of the capabilities of Azure and how you can start building an end to end BI proof-of-concept today.
This document provides an introduction to relational databases, NoSQL databases, and data in general. It includes the following:
- An overview of relational databases and their ACID properties. Relational databases are best for structured, centralized data and scale vertically.
- A survey of several popular NoSQL databases like MongoDB, Cassandra, Redis, and HBase. NoSQL databases are best for unstructured, large quantities of data and scale horizontally.
- General advice that the data and query models, durability needs, scalability needs, and consistency requirements should determine the best database choice. Trying different options is recommended.
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021Sandesh Rao
The document discusses Oracle Machine Learning (OML) services on Oracle Autonomous Database. It provides an overview of the OML services REST API, which allows storing and deploying machine learning models. It enables scoring of models using REST endpoints for application integration. The API supports classification/regression of ONNX models from libraries like Scikit-learn and TensorFlow. It also provides cognitive text capabilities like topic discovery, keywords, sentiment analysis and text summarization.
Oracle NoSQL DB & InfiniteGraph - Trends in Big Data and Graph TechnologyInfiniteGraph
Join Oracle NoSQL DB and InfiniteGraph development teams in a discussion of the latest trends in Big Data and Graph Technology. Learn what Oracle’s view of Big Data is and how Oracle NoSQL Database technologies enable you to manage vast amounts of real-time key-value data.
Join Objectivity, Inc.’s VP of Product Management, Brian Clark, in a discussion of the latest trends in Big Data Analytics, defining what is Big Data and understanding how to maximize your existing architectures by utilizing NOSQL technologies to improve functionality and provide real-time results. There will be a focus on relationship analytics as well as an introduction to NOSQL data stores, object and graph databases, such as the architecture behind Objectivity/DB and InfiniteGraph.
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
The document provides an overview of Big Data technology landscape, specifically focusing on NoSQL databases and Hadoop. It defines NoSQL as a non-relational database used for dealing with big data. It describes four main types of NoSQL databases - key-value stores, document databases, column-oriented databases, and graph databases - and provides examples of databases that fall under each type. It also discusses why NoSQL and Hadoop are useful technologies for storing and processing big data, how they work, and how companies are using them.
This document provides an overview of graph databases and Neo4j. It discusses how graph databases are better suited than relational databases for interconnected data and have simpler data models. Neo4j is highlighted as a graph database that uses nodes, edges and properties to represent data and uses the Cypher query language. It is fully ACID compliant, open source, and has a large active community.
This document discusses trends driving the adoption of NoSQL databases, including increasing data size, connectivity of information, semi-structured data, and distributed application architectures. It describes four categories of NoSQL databases - aggregate-oriented, key-value stores, column family (BigTable), and document databases - and provides examples and comparisons of their pros and cons.
Oracle Week 2016 - Modern Data ArchitectureArthur Gimpel
This document discusses modern operational data architectures and the use of both relational and NoSQL databases. It provides an overview of relational databases and their ACID properties. While relational databases dominate the market, they have limitations around scalability, flexibility, and performance. NoSQL databases offer alternatives like horizontal scaling and flexible schemas. Key-value stores are best for caching, sessions, and serving data, while document stores are popular for hierarchical and search use cases. Graph databases excel at link analysis. The document advocates a polyglot persistence approach using multiple database types according to their strengths. It provides examples of search architectures using both database-centric and application-centric distribution approaches.
Data Lake Acceleration vs. Data Virtualization - What’s the difference?Denodo
Watch full webinar here: https://bit.ly/3hgOSwm
Data Lake technologies have been in constant evolution in recent years, with each iteration primising to fix what previous ones failed to accomplish. Several data lake engines are hitting the market with better ingestion, governance, and acceleration capabilities that aim to create the ultimate data repository. But isn't that the promise of a logical architecture with data virtualization too? So, what’s the difference between the two technologies? Are they friends or foes? This session will explore the details.
This document discusses big data and applying semantics to unstructured data. It provides an overview of big data, including what big data is, why it is important now due to cheaper hardware and software, and examples like Hadoop. It also discusses NoSQL databases and semantic tools to help analyze and understand large, unstructured data sets. Key recommendations are to aim high and leverage the big data opportunities, build consensus on opportunities and risks, and focus on developing important human skills like critical thinking to help analyze large data sets.
"Get Ready for Big Data" presentation from Gilbane Boston 2011; for more details, see http://gilbaneboston.com/conference_program.html#t2 and http://pbokelly.blogspot.com/2011/12/gilbane-boston-2011-big-data.html
Big Data is the reality of modern business: from big companies to small ones, everybody is trying to find their own benefit. Big Data technologies are not meant to replace traditional ones, but to be complementary to them. In this presentation you will hear what is Big Data and Data Lake and what are the most popular technologies used in Big Data world. We will also speak about Hadoop and Spark, and how they integrate with traditional systems and their benefits.
This document provides an introduction to NoSQL databases, including the motivation behind them, where they fit, types of NoSQL databases like key-value, document, columnar, and graph databases, and an example using MongoDB. NoSQL databases are a new way of thinking about data that is non-relational, schema-less, and can be distributed and fault tolerant. They are motivated by the need to scale out applications and handle big data with flexible and modern data models.
Elliott Cordo, Principal Consultant at Caserta Concepts, delivered a talk on NoSQL data storage architectures at our most recent Big Data Warehousing Meetup: what they are, how they're used and why you can't ignore them in the context of existing enterprise data ecosystems.
For more information, check out our website at http://www.casertaconcepts.com/.
Extract business value by analyzing large volumes of multi-structured data from various sources such as databases, websites, blogs, social media, smart sensors...
Hadoop and the Data Warehouse: When to Use Which DataWorks Summit
In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages.
Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications.
Two of the most trusted experts in their fields, Steve Wooledge, VP of Product Marketing from Teradata and Jim Walker of Hortonworks will examine how big data technologies are being used today by practical big data practitioners.
NoSQL is a non-relational database approach that accommodates a wide variety of data models. It is non-relational, distributed, flexible and scalable. The four main types of NoSQL databases are document databases, key-value stores, column-oriented databases, and graph databases. MongoDB is an example of a document-oriented NoSQL database. NoSQL databases offer benefits over relational databases like flexible schemas, horizontal scalability, and fast queries. Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its parallel programming model and the Hadoop Distributed File System for storage.
Slides for the talk at AI in Production meetup:
https://www.meetup.com/LearnDataScience/events/255723555/
Abstract: Demystifying Data Engineering
With recent progress in the fields of big data analytics and machine learning, Data Engineering is an emerging discipline which is not well-defined and often poorly understood.
In this talk, we aim to explain Data Engineering, its role in Data Science, the difference between a Data Scientist and a Data Engineer, the role of a Data Engineer and common concepts as well as commonly misunderstood ones found in Data Engineering. Toward the end of the talk, we will examine a typical Data Analytics system architecture.
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response.
2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents.
3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
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 .
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
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.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
5. A Typical “Big Data” Analytics Setup
Data Aggregation and Analytics Applications
Commodity Linux Platforms and/or High Performance Computing Clusters
Column Data Graph Object K-V
RDBMS Hadoop Doc DB
Store W/H DB DB Store
Structured Semi-Structured Unstructured
7. Not Only SQL – a group of 4 primary technologies
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Users choose between four different primary technologies for different
purposes:
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Key-Value Stores
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“Big Table” Clones
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Document Databases
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Object and Graph databases (including InfiniteGraph)
•
Many implementations sacrifice consistency (ACID transactions, CAP
– eventual consistency) for performance.
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Technologies such as Objectivity/DB and InfiniteGraph offer ACID
transactions, with consistency and performance.
9. Key-Value Stores
“Dynamo: Amazon’s High Available Key-Value Store” [2007]
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Data model:
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Global key-value mapping
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Scalable (sharded) HashMap KEY VALUE
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Highly fault tolerant (typically)
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Examples:
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Riak, Redis and Voldemort
10. Key-Value Stores: Pros & Cons
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Strengths:
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Simple data model
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Great at scaling out horizontally
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Scalable
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Available
KEY VALUE
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Weaknesses:
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Simplistic data model
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Poor for complex data
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Unsuited for interconnected data
11. Big Table Clones – Column Family
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Google’s “Bigtable: A Distributed Storage System for
Structured Data” [2006]
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Column-Family are essentially Big Table clones.
Column
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Data Model: KEY Column Name Value D/Time
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A big table, with column families.
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Map-reduce for parallel query/processing.
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Examples:
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Hbase, HyperTable and Cassandra.
12. Big Table Clones – Pros & Cons
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Strengths:
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Data model supports semi-structured data
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Naturally indexed (columns)
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Good at scaling out horizontally
Column
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Weaknesses:
KEY Column Name Value D/Time
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Complex data model
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Unsuited for highly interconnected data
13. Document Databases
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Data Model:
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A collection of unstructured or semi-structured documents.
–
Each document is referenced using a key-value pair.
–
The “value” can range from unstructured text to a collection of key-
value pairs or a group of XML objects.
–
Index-centric to support queries based on content.
•
Examples:
KEY DOCUMENT
–
CouchDB and MongoDB.
14. Document Databases – Pros & Cons
•
Strengths:
–
Simple, powerful data model
–
Good scalability if sharding is supported
•
Weaknesses: KEY DOCUMENT
–
Unsuited for interconnected data
–
Query model limited is to keys and indexes
–
Generally uses Map-Reduce (designed for batch operations) for
larger queries
15. Object Databases
•
Data Model [ODMG'93]:
–
Objects have a Class (type) and a group of Values
–
Each Object instance has a unique Object Identifier [OID]
–
Connections use Object Identifiers for efficiency
–
Supports class inheritance and polymorphism
•
Examples:
OID OBJECT
–
Objectivity/DB and db4objects
Connections
16. Object Databases – Pros & Cons
•
Strengths:
–
Simple, powerful data model that includes inheritance and
polymorphism
–
Every object has a class (type) and a unique Object Identifier
–
Good scalability if sharding is supported
–
Uses Object Identifiers instead of JOIN tables to support very fast
navigational operations OID OBJECT
Connections
•
Weaknesses:
–
The query language never became a standard
–
Supports standard object oriented languages but isn't supported by
a wide range of third party tools in the way that SQL is.
17. Graph Databases
•
Data model:
–
Node (Vertex) and Relationship (Edge) objects
–
Directed
–
May be a hypergraph (edges with multiple endpoints)
•
Examples:
–
InfiniteGraph, Neo4j, OrientDB, AllegroGraph, TitanDB and Dex
2 N
VERTEX EDGE
18. Graph Databases – Pros & Cons
•
Strengths:
–
Extremely fast for connected data
–
Scales out, typically
–
Easy to query (navigation)
–
Simple data model
•
Weaknesses:
–
May not support distribution or sharding
–
Requires conceptual shift... a different way of thinking
2 N
VERTEX EDGE
20. Typical “Big Data” Analytics Phases
Analytics and
Front-End Processing Repository Visualization Tools
The strategic competitors are all moving in the same direction
21. Incremental Improvements Aren’t Enough
All current solutions use the same basic architectural model
• None of the current solutions have a way to store connections between
entities in different silos
• Most analytic technology focuses on the content of the data nodes,
rather than the many kinds of connections between the nodes and the
data in those connections
• Why? Because relational and most NoSQL solutions are bad at handling
relationships.
• Object and Graph databases can efficiently store, manage and query the
many kinds of relationships hidden in the data.
26. Example 4 - Ad Placement Networks
Smartphone Ad placement - based on the the user’s profile and location data
captured by opt-in applications.
• The location data can be stored and distilled in a key-value and column store
hybrid database, such as Cassandra
• The locations are matched with geospatial data to deduce user interests.
• As Ad placement orders arrive, an application built on a graph database such
as InfiniteGraph, matches groups of users with Ads:
• Maximizes relevance for the user.
• Yields maximum value for the advertiser and the placer.
27. Example 5 - Healthcare Informatics
Problem: Physicians need better electronic records for managing patient data on a global
basis and match symptoms, causes, treatments and interdependencies to improve
diagnoses and outcomes.
• Solution: Create a database capable of leveraging existing architecture using NOSQL tools
such as Objectivity/DB and InfiniteGraph that can handle data capture, symptoms,
diagnoses, treatments, reactions to medications, interactions and progress.
• Result: It works:
• Diagnosis is faster and more accurate
• The knowledge base tracks similar medical cases.
• Treatment success rates have improved.
28. Relationship (Connection) Analytics...
Relational Database
Think about the SQL query for finding all links between the two “blue” rows... Good luck!
Table_A Table_B Table_C Table_D Table_E Table_F Table_G
Relational databases aren’t good at handling complex relationships!
29. Relationship (Connection) Analytics...
Relational Database
Think about the SQL query for finding all links between the two “blue” rows... Good luck!
Table_A Table_B Table_C Table_D Table_E Table_F Table_G
Objectivity/DB or InfiniteGraph - The solution can be found with a few lines of code
A3 G4
32. Lesson 1 – The Repository Matters A Lot
NEED RDBMS Key- Column Document ODBMS Graph
Value Family Database Database
OLTP YES No Maybe No Maybe No
Text No No No YES Maybe No
Handling
Multimedia No Maybe No Maybe YES Maybe
Engineering/ No No No No YES Maybe
Scientific
Business YES No Maybe No Maybe Maybe
Intelligence
Log Maybe No Maybe No YES Maybe
Processing
Connection No No No No Maybe YES
Handling/
Analysis
33. Lesson 2 – Languages and Tools Matter Too
NEED Repository Language BI Tools Visual
Analytics
OLTP RDBMS SQL, Java YES Maybe
Text Document Java, XML No Maybe
Database
Multimedia ODBMS Java, C++ No Maybe
Eng/Science ODBMS C,C++, R Maybe YES
Fortran
Business RDBMS Java, SQL, R YES YES
Intelligence
Log NoSQL, C++, R, Maybe YES
Processing ODBMS Java, SQL
Connection Graph Java, C++, Maybe YES
Handling/ Database SPARQL
Analysis
34. SUMMARY: A Polyglot Approach Works Best...
LANGUAGE REPOSITORY
PROBLEM
ANALYTICS
BI TOOLS GRAPH TOOLS VISUAL ANALYTICS
38. InfiniteGraph - The Enterprise Graph Database
• A high performance distributed database engine that supports analyst-time decision
support and actionable intelligence
• Cost effective link analysis – flexible deployment on commodity resources (hardware
and OS).
• Efficient, scalable, risk averse technology – enterprise proven.
• High Speed parallel ingest to load graph data quickly.
• Parallel, distributed queries
• Flexible plugin architecture
• Complementary technology
• Fast proof of concept – easy to use Graph API.
39. Objectivity/DB
A distributed, object database built for handling data with many complex relationships.
• Reliable - Deployed in process control, telecom and medical equipment, Big Science,
complex financial, defense and Intelligence Community applications.
• Provably scalable - used to build the World’s first Petabyte+ database at Stanford
Linear Accelerator in the year 2000.
• Advanced query capabilities - Parallel Query Engine
• Interoperable - across languages and platforms
–
C++, C#, Java, Python and SQL++
–
Linux, Mac OS X and Windows (32 and 64-bit)
40. The Big Data Connection Platform
Data Visualization
& Analytics
*Now HP *Now IBM
Big Data Connection
Platform
Processing Platform
*Now EMC *Now IBM *Now IBM
*Now Teradata *Now HP
*Now SAP
Connectors /
Integration
Servers /
File Storage *Now Oracle
41. The Big Data Connection Platform
Data Visualization
& Analytics
*Now HP *Now IBM
Big Data Connection
Platform
Processing Platform
*Now EMC *Now IBM *Now IBM
*Now Teradata *Now HP
*Now SAP
Connectors /
Integration
Servers /
File Storage *Now Oracle
42. Thank You!
Please take a look at objectivity.com
For Online Demos, White Papers, Free Downloads,
Samples & Tutorials
You Can Also See Us At NoSQL Now!
In San Jose, CA on August 22
Editor's Notes
Thinking we should be less about Objy in the last bullet… possibly Object oriented and graph databases… ?
Note Object Oriented Databases as NOSQL here.
By initiating a polyglot approach – One can utilize existing SQL based architecture and databases while still gaining the competitive advantage that the latest NOSQL technologies provide. One example of this Polyglot approach is shown here. The technology(ies) used would be dependent on the use case.
By initiating a polyglot approach – One can utilize existing SQL based architecture and databases while still gaining the competitive advantage that the latest NOSQL technologies provide. One example of this Polyglot approach is shown here. The technology(ies) used would be dependent on the use case.
By initiating a polyglot approach – One can utilize existing SQL based architecture and databases while still gaining the competitive advantage that the latest NOSQL technologies provide. One example of this Polyglot approach is shown here. The technology(ies) used would be dependent on the use case.
By initiating a polyglot approach – One can utilize existing SQL based architecture and databases while still gaining the competitive advantage that the latest NOSQL technologies provide. One example of this Polyglot approach is shown here. The technology(ies) used would be dependent on the use case.
This section seems out of place.
By having a scalable and distributed platform that can manage connections between all types of disparate data, enterprise can easily capitalize on the best tools for the job at hand.
By having a scalable and distributed platform that can manage connections between all types of disparate data, enterprise can easily capitalize on the best tools for the job at hand.