This document discusses amaysim's implementation of Amazon Redshift, Alteryx, and Tableau for data analytics. It provides an overview of each tool and how amaysim uses them together in their business intelligence stack. Key points include:
- Amaysim uses Redshift for data warehousing, Alteryx to prepare and blend data, and Tableau for visualization and self-service analytics. This allows for analysis within hours rather than weeks.
- With a small analytics team, the tools empower line of business users to solve their own problems quickly. This increases workforce productivity.
- Lessons learned include democratizing analytics, making tools relevant to different stakeholders, and celebrating successes to drive cultural
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.
How to deploy Apache Spark in a multi-tenant, on-premises environmentBlueData, Inc.
Adoption of Apache Spark in the enterprise is increasing rapidly - it's become one of the fastest growing and most popular technologies in the Big Data ecosystem.
However, implementing an enterprise-ready, on-premises Spark deployment can be very complex and it requires expertise that is generally not available to all.
BlueData makes it easier to deploy Apache Spark on-premises. With BlueData, you can spin up virtual Spark clusters within minutes – providing secure, self-service, on-demand access to Big Data analytics and infrastructure. You can deploy Spark in standalone mode or with Hadoop / YARN. You can also build analytical pipelines and create Spark clusters using our RESTful APIs, and use web-based Zeppelin notebooks for interactive data analytics.
BlueData’s software platform leverages virtualization and Docker containers – combined with our own patent-pending innovations – to make it faster, and more cost-effective for enterprises to get up and running with a multi-tenant Spark deployment on-premises.
Learn more at www.bluedata.com
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Big data requires service that can orchestrate and operationalize processes to refine the enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that's built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Migrating Databases to the Cloud: Introduction to AWS DMS - SRV215 - Chicago ...Amazon Web Services
In this introductory session, we cover how to convert and migrate your relational databases, non-relational databases, and data warehouses to the cloud. AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT) have been used to migrate tens of thousands of databases across the world. This includes homogeneous migrations, such as PostgreSQL to PostgreSQL, and heterogeneous migrations between different database engines, such as Oracle or SQL Server to Amazon Aurora, Amazon DynamoDB, and Amazon Redshift. Learn how to quickly and securely migrate your data and procedural code, enjoy flexibility and cost savings, and minimize the downtime of your applications.
In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Event: Passcamp, 07.12.2017
Speaker: Stefan Kirner
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel: https://www.inovex.de/blog
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDogRedis Labs
Think you have big data? What about high availability
requirements? At DataDog we process billions of data points every day including metrics and events, as we help the world
monitor the their applications and infrastructure. Being the world’s monitoring system is a big responsibility, and thanks to
Redis we are up to the task. Join us as we discuss how the DataDog team monitors and scales Redis to power our SaaS based monitoring offering. We will discuss our usage and deployment patterns, as well as dive into monitoring best practices for production Redis workloads
Big Data Testing : Automate theTesting of Hadoop, NoSQL & DWH without Writing...RTTS
Testing of Hadoop, NoSQL and Data Warehouses Visually
-----------------------------------------------------------------------------
We just made automated data testing really easy. Automate your Big Data testing visually, with no programming needed.
See how to automate Hadoop, No SQL and Data Warehouse testing visually, without writing any SQL or HQL. See how QuerySurge, the leading Big Data testing solution, provides novices and non-technical team members with a fast & easy way to be productive immediately while speeding up testing for team members skilled in SQL/HQL.
This webinar is geared towards:
- Big Data & Data Warehouse Architects, ETL Developers
- ETL Testers, Big Data Testers
- Data Analysts
- Operations teams
- Business Intelligence (BI) Architects
- Data Management Officers & Directors
You will learn how to:
• Improve your Data Quality
• Accelerate your data testing cycles
• Reduce your costs & risks
• Realize a huge ROI
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
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.
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting (in its original format) and extract value. In this session, learn how to architect and implement a data lake in the AWS Cloud. Learn about best practices as we walk through architectural blueprints.
From weeks to hours big data analytics with tableau and amazon web services ...Amazon Web Services
Amazon Web Services and Tableau Software have shifted how organizations store and access their data. The fast, scalable, and cost efficient services that Amazon Web Services provides for housing data combined with Tableau's visual analytics solution means that within hours an organization can securely put the power of their massive data assets into the hands of their domain experts removing expensive overhead and lengthy setup-up time. Go from a petabyte scale data warehouse setup to leveraging visual analytics in just a couple of hours. Learn how leaders in managing big data are taking advantage of disruptive technology.
In this presentation you'll learn how to:
Empower visual data discovery against big data via a live demo of AWS and Tableau working together
Revolutionize corporate reporting and dashboards, including examples of customer case studies.
Promote data driven decision making at every level
Speaker: Jason Oakes, Sales Consultant, Tableau
Next generation analytics isn’t on its way… it’s already arrived. Most businesses are in the process of developing their new data platforms on the cloud, or moving their existing analytics infrastructure to the cloud. Attend this webinar to learn model architectures and best practices for analytics on AWS. You’ll also learn how you can leverage cloud to spread insight throughout your organization.
Join us to learn:
• What cloud data infrastructure should look like
• How to optimize your analytics deployment on the cloud
• Using Tableau to find and share new insights with everyone in your organization
How to deploy Apache Spark in a multi-tenant, on-premises environmentBlueData, Inc.
Adoption of Apache Spark in the enterprise is increasing rapidly - it's become one of the fastest growing and most popular technologies in the Big Data ecosystem.
However, implementing an enterprise-ready, on-premises Spark deployment can be very complex and it requires expertise that is generally not available to all.
BlueData makes it easier to deploy Apache Spark on-premises. With BlueData, you can spin up virtual Spark clusters within minutes – providing secure, self-service, on-demand access to Big Data analytics and infrastructure. You can deploy Spark in standalone mode or with Hadoop / YARN. You can also build analytical pipelines and create Spark clusters using our RESTful APIs, and use web-based Zeppelin notebooks for interactive data analytics.
BlueData’s software platform leverages virtualization and Docker containers – combined with our own patent-pending innovations – to make it faster, and more cost-effective for enterprises to get up and running with a multi-tenant Spark deployment on-premises.
Learn more at www.bluedata.com
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Big data requires service that can orchestrate and operationalize processes to refine the enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that's built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Migrating Databases to the Cloud: Introduction to AWS DMS - SRV215 - Chicago ...Amazon Web Services
In this introductory session, we cover how to convert and migrate your relational databases, non-relational databases, and data warehouses to the cloud. AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT) have been used to migrate tens of thousands of databases across the world. This includes homogeneous migrations, such as PostgreSQL to PostgreSQL, and heterogeneous migrations between different database engines, such as Oracle or SQL Server to Amazon Aurora, Amazon DynamoDB, and Amazon Redshift. Learn how to quickly and securely migrate your data and procedural code, enjoy flexibility and cost savings, and minimize the downtime of your applications.
In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Event: Passcamp, 07.12.2017
Speaker: Stefan Kirner
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel: https://www.inovex.de/blog
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDogRedis Labs
Think you have big data? What about high availability
requirements? At DataDog we process billions of data points every day including metrics and events, as we help the world
monitor the their applications and infrastructure. Being the world’s monitoring system is a big responsibility, and thanks to
Redis we are up to the task. Join us as we discuss how the DataDog team monitors and scales Redis to power our SaaS based monitoring offering. We will discuss our usage and deployment patterns, as well as dive into monitoring best practices for production Redis workloads
Big Data Testing : Automate theTesting of Hadoop, NoSQL & DWH without Writing...RTTS
Testing of Hadoop, NoSQL and Data Warehouses Visually
-----------------------------------------------------------------------------
We just made automated data testing really easy. Automate your Big Data testing visually, with no programming needed.
See how to automate Hadoop, No SQL and Data Warehouse testing visually, without writing any SQL or HQL. See how QuerySurge, the leading Big Data testing solution, provides novices and non-technical team members with a fast & easy way to be productive immediately while speeding up testing for team members skilled in SQL/HQL.
This webinar is geared towards:
- Big Data & Data Warehouse Architects, ETL Developers
- ETL Testers, Big Data Testers
- Data Analysts
- Operations teams
- Business Intelligence (BI) Architects
- Data Management Officers & Directors
You will learn how to:
• Improve your Data Quality
• Accelerate your data testing cycles
• Reduce your costs & risks
• Realize a huge ROI
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
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.
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting (in its original format) and extract value. In this session, learn how to architect and implement a data lake in the AWS Cloud. Learn about best practices as we walk through architectural blueprints.
From weeks to hours big data analytics with tableau and amazon web services ...Amazon Web Services
Amazon Web Services and Tableau Software have shifted how organizations store and access their data. The fast, scalable, and cost efficient services that Amazon Web Services provides for housing data combined with Tableau's visual analytics solution means that within hours an organization can securely put the power of their massive data assets into the hands of their domain experts removing expensive overhead and lengthy setup-up time. Go from a petabyte scale data warehouse setup to leveraging visual analytics in just a couple of hours. Learn how leaders in managing big data are taking advantage of disruptive technology.
In this presentation you'll learn how to:
Empower visual data discovery against big data via a live demo of AWS and Tableau working together
Revolutionize corporate reporting and dashboards, including examples of customer case studies.
Promote data driven decision making at every level
Speaker: Jason Oakes, Sales Consultant, Tableau
Next generation analytics isn’t on its way… it’s already arrived. Most businesses are in the process of developing their new data platforms on the cloud, or moving their existing analytics infrastructure to the cloud. Attend this webinar to learn model architectures and best practices for analytics on AWS. You’ll also learn how you can leverage cloud to spread insight throughout your organization.
Join us to learn:
• What cloud data infrastructure should look like
• How to optimize your analytics deployment on the cloud
• Using Tableau to find and share new insights with everyone in your organization
Tableau Software - Business Analytics and Data Visualizationlesterathayde
Tableau boasts drag-and-drop features that allow users to visualize information from any structured format. Tableau is the only provider of data visualization and business intelligence software that can be installed and used by anyone while also adhering to IT standards making it the fastest growing tool on the planet for Business Intelligence. Gartner has recently named us in the magic Quadrant among the Top 27 vendors for BI tool. We are no 1 in ease of use, no 1 in reporting and dashboard creation, interactive visualization, etc.
. Feel free to download the product, see the sample reports & dashboards for other industries from
http://www.tableausoftware.com
Please use the below link to download a 15 Day trial version of Tableau Desktop and Server Versions.
http://www.tableausoftware.com/products/trial
You can also do a self-training by going through the Videos in the below link.
http://www.tableausoftware.com/learn/training.
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
TekSlate is the leader in Tableau tutorials and other business intelligence tutorials emphasis on delivering complete knowledge through self-paced learning. Tableau Free Tutorials tech to create highly interactive dashboards using actions.
To Learn More Click On Below Link:
http://bit.ly/1zKKnPm
The Datafication of HR in 2016: Graduating From Metrics to AnalyticsHuman Capital Media
With recent articles like Harvard Business Review’s “It’s Time to Split HR,” written by world-renowned business adviser and author Ram Charan, and Bersin Insights’ “Will HR Lose the Battle Over Analytics,” written by industry analyst Karen O’Leonard, 2016 represents a pivotal year for human resources.
The global economic recovery, compounded by demographic shifts, is moving power from employers to employees, turning labor into a seller’s market. As a result, the workforce is becoming an increasingly core strategic consideration to businesses. Yet the most commonly monitored workforce metrics do very little to deliver true insight into workforce topics. Leaders need to graduate from metrics to analytics, surfacing the important connections and patterns in their data to make better workforce decisions.
Join expert Dave Weisbeck as he discusses how HR can play a more critical role driving business performance than ever before. In this informative webinar, Weisbeck will discuss how you can graduate from metrics to analytics, ramping up from operational reporting to strategic analytics and planning.
In this session, participants will learn:
Trends shaping the datafication of HR, including the case for and against splitting HR.
How HR can climb the workforce intelligence maturity curve, defining key terms and concepts.
The future of HR as a strategic advisor, with examples of how to graduate from metrics to analytics in: Recruiting effectiveness, Performance management, Talent retention, Comp and benefits, and Workforce costs
Common pitfalls to avoid.
Building enterprise advance analytics platformHaoran Du
By Raymond Fu - Practice Architect
This lecture talks about the best practices in building an advanced analytics platform to help companies apply machine learning, deep learning and data science to their structured and unstructured data.
At Southern California Data Science Conference Sept.25.2016 at USC
http://socaldatascience.org/
http://www.datalaus.com/en/
Logging is one of those things that everyone complains about, but doesn't dedicate time to. Of course, the first rule of logging is "do it". Without that, you have no visibility into system activities when investigations are required. But, the end goal is much, much more than this. Almost all applications require security audit logs for compliance; application logs for visibility across all cloud properties; and application tracing for tracking usage patterns and business intelligence. The latter is that magic sauce that helps businesses learn about their customer or in some cases the data is FOR the customer. Without a strategy this can get very messy, fast. In this session Michele will discuss design patterns for a sound logging and audit strategy; considerations for security and compliance; the benefits of a noSQL approach; and more.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/39AhUB7
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/3fpitC3
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Amazon Web Services proporciona una amplia gama de servicios que le ayudarán a crear e implementar aplicaciones de análisis de big data de forma rápida y sencilla. AWS ofrece un acceso rápido a recursos de TI económicos y flexibles, algo que permitirá escalar prácticamente cualquier aplicación de big data con rapidez, incluidos almacenamiento de datos, análisis de clics, detección de elementos fraudulentos, motores de recomendación, proceso ETL impulsado por eventos, informática sin servidor y procesamiento del Internet de las cosas. Con AWS no necesita hacer grandes inversiones iniciales de tiempo o dinero para crear y mantener la infraestructura. En su lugar, puede aprovisionar exactamente el tipo y el tamaño adecuado de los recursos que necesita para impulsar sus aplicaciones de análisis de big data. Puede obtener acceso a tantos recursos como necesite, prácticamente al instante, y pagar únicamente por los utilice.
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Strategy session 5 - unlocking the data dividend - andy steerAndy Steer
"A recent study completed by IDC examined the economic benefits accrued to organisations that made basic levels of investment in distinct areas of analytics and data management compared with the benefits accrued by organisations that opted for a broader and more diverse set of investments. The conclusion was that the leading organisations expect to capture in excess of $1.5 trillion more in value from their data and analytics initiatives over the next 4 years. This represents a 60% higher data dividend for the leading organisations.
To achieve these benefits organisations need to embrace the changing reality of the new data driven society and make a break from the beliefs and best practices inherent in traditional Business Intelligence programmes.
During the presentation Andy will expand on the data dividend concept, outline the 4 key investment areas that should be getting your attention and perhaps most importantly, explain how your existing SAP BusinessObjects technology can help you take your share of the estimated £53 billion UK data dividend."
IT + Line of Business - Driving Faster, Deeper Insights TogetherDATAVERSITY
Marketo helps customers master the science of digital marketing with the analytics it provides customers. Internally, Marketo found itself afflicted with “Excel mania” and suffering from the side effects that come with it, including slow time to insights and hours lost on mundane but critical data prep. This quickly changed when they bet their BI strategy on Alteryx, Amazon Web Services (AWS), and Tableau.
Join us and hear from Tim Chandler, head of BI and data solutions, and learn how:
the stack is enabling more efficient analytics processes, as well as providing governance and scalability
IT and line of business (LOB) are effectively working together to uncover more insights, faster – saving time and resources in the process
an enterprise-class data architecture is driving business engagement and dashboard adoption across the entire company
Register now to learn how you can improve your analytics processes - leading to faster, deeper insights.
in this slide i have tried to explain what an data engineer does and what is the difference between a data engineer and a data analytics and data scientist
AWS Webcast - Sales Productivity Solutions with MicroStrategy and RedshiftAmazon Web Services
Sales Force Automation (SFA) and Customer Relationship Management (CRM) tools, such as Salesforce.com and Microsoft Dynamics CRM, are ubiquitous tools that provide all of the transactional capabilities required to manage a company's sales pipeline. SFA and CRM data alone, however, is limited and so combining it with information from other sources enables you to create unique and powerful insights. When combined with product and financial data, for example, get visibility into relationships between geographies, sales reps, product performance, and revenue to ultimately optimize profits. Layer on advanced analytic to make predictions about future product sales based on seasonality and other market conditions. To unleash the full power of the CRM and dramatically increase operational performance and top-line revenue, companies are leveraging advanced analytic and data visualization to deliver new insights to the entire sales organization. Moreover, delivering these sales enablement productivity solutions on mobile devices, ensures strong adoption across every sales team. Join us in this webinar to learn how to use MicroStrategy together with Amazon Redshift to build mobile sales productivity solutions for your business.
Against the backdrop of Big Data, the Chief Data Officer, by any name, is emerging as the central player in the business of data, including cybersecurity. The MITCDOIQ Symposium explored the developing landscape, from local organizational issues to global challenges, through case studies from industry, academic, government and healthcare leaders.
Joe Caserta, president at Caserta Concepts, presented "Big Data's Impact on the Enterprise" at the MITCDOIQ Symposium.
Presentation Abstract: Organizations are challenged with managing an unprecedented volume of structured and unstructured data coming into the enterprise from a variety of verified and unverified sources. With that is the urgency to rapidly maximize value while also maintaining high data quality.
Today we start with some history and the components of data governance and information quality necessary for successful solutions. I then bring it all to life with 2 client success stories, one in healthcare and the other in banking and financial services. These case histories illustrate how accurate, complete, consistent and reliable data results in a competitive advantage and enhanced end-user and customer satisfaction.
To learn more, visit www.casertaconcepts.com
Retrieving and managing data effectively is crucial to gain useful information and allow for the best decision making. But how long does it take you to get the information?
First, ETL time is usually a pain point as the size of data is often huge. Second, query time is critical as well, especially for ad hoc analyses.
Say goodbye to all this waiting!
Sadas Engine was specifically designed to achieve outstanding performances in DWH environments.
Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. Using our cloud-based service you can easily connect to your data, perform advanced analysis, and create stunning visualizations and rich dashboards that can be accessed from any browser or mobile device.
Business intelligence and analytics both refer to maximize the value of your data to make better decisions, ALTEN CAlsoft Labs helps
enterprises accelerate business intelligence by providing the most comprehensive, integrated and easy-to-use reporting and analytics features with its industry specific analytics solutions and best in-class technology.
Analytic Excellence - Saying Goodbye to Old ConstraintsInside Analysis
The Briefing Room with Dr. Robin Bloor and Actian
Live Webcast August 6, 2013
http://www.insideanalysis.com
With all the innovations in compute power these days, one of the hardest hurdles to overcome is the tendency to think in old ways. By and large, the processing constraints of yesterday no longer apply. The new constraints revolve around the strategic management of data, and the effective use of business analytics. How can your organization take the helm in this new era of analysis?
Register for this episode of The Briefing Room to find out! Veteran Analyst Wayne Eckerson of The BI Leadership Forum, will explain how a handful of key innovations has significantly changed the game for data processing and analytics. He'll be briefed by John Santaferraro of Actian, who will tout his company's unique position in "scale-up and scale-out" for analyzing data.
Similar to Analyzing Billions of Data Rows with Alteryx, Amazon Redshift, and Tableau (20)
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
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
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
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
6. Relational data warehouse
Massively parallel; Petabyte scale
Fully managed
HDD and SSD Platforms
$1,000/TB/Year; starts at $0.25/hour
Amazon
Redshift
a lot faster
a lot simpler
a lot cheaper
What is Amazon Redshift?
7. Amazon Redshift is easy to use
• Provision in minutes
• Monitor query performance
• Point and click resize
• Built in security
• Automatic backups
8. Amazon Redshift architecture
Leader Node
Simple SQL end point
Stores metadata
Optimizes query plan
Coordinates query execution
Compute Nodes
Local columnar storage
Parallel/distributed execution of all queries, loads,
backups, restores, resizes
Start at just $0.25/hour, grow to 2 PB (compressed)
DC1: SSD; scale from 160 GB to 326 TB
DS2: HDD; scale from 2 TB to 2 PB
Ingestion/Backup
Backup
Restore
JDBC/ODBC
10 GigE
(HPC)
9. The Amazon Redshift view of data warehousing
• 10x cheaper
• Easy to provision
• Higher DBA productivity
• 10x faster
• No programming
• Easily leverage BI tools,
Hadoop, Machine Learning,
Streaming
• Analysis in-line with process
flows
• Pay as you go, grow as you
need
• Managed availability & DR
Enterprise Big Data SaaS
10. The legacy view of data warehousing ...
Global 2,000 companies
Sell to central IT
Multi-year commitment
Multi-year deployments
Multi-million dollar deals
11. … Leads to dark data
This is a narrow view
Small companies also have big data
(mobile, social, gaming, adtech, IoT)
Long cycles, high costs, administrative
complexity all stifle innovation
0
200
400
600
800
1000
1200
Enterprise Data Data in Warehouse
12. Please note:
All data used for this presentation is sample data only. It is used to demonstrate the capability of AWS,
Alteryx and Tableau, and no actual amaysim data will be shared.
14. amaysim is an Australian mobile virtual network
operator established in November 2010.
About me
Adrian Loong – Business Intelligencemanager
• Responsible for analytics strategy and execution
across Finance, Retail Sales, Marketing and HR
• Expertise:
• Decision support / Management consulting
• Visualisation, Financial analytics, Budgeting &
Forecasting
• CPA
.
About amaysim
15. Strategy - Data driven decisions in real time
1. Knowledge is power
2. Smart management =
happy customers &
happy shareholders
3. Get a real competitive
edge - stop looking back
and start predicting the
future
4. A 3 year rolling forecast
in real time – generate
real company wealth by
being reliable
16. How Amaysim has benefited from their analytics program
Workforce Productivity
• We have a 3 person analytics team covering wide span of functions (Finance, Marketing –
Customer retention & acquisition, Sales – Retail and Online, Data warehousing, HR)
• By enabling line of business users to quickly build on a baseline of analytics, they can easily
solve their own specific business problems quickly and do not have to wait on Business
intelligence teams
Reduced time to insight
• We are able to get the data we need faster. Projects that would have taken 2-3 weeks are
now down to a day
Data driven decision making
• Line of business users are able to get direct access to their own data in an easy visualization
enabling them to able to solve problems faster.
• People can look at the data, do some discovery, and then arrive at an answer
18. amaysim has a wide variety of data sources and a lot of data
being generated daily
Wide variety of data sources A lot of data is generated daily
Source Systems
Phone call
Internet
data traffic
SMS
Over 10 billion rows of data with
nearly 20 million rows added daily
Databases
19. Competition is intense in the mobile sector
Major carriers Mobile Virtual Network Operators
20. We only have a small team
covering a wide span of functions
Retail Sales
Finance
Customer
acquisition
HR
Photo Source: http://www.timeshighereducation.co.uk/news/business-
schools-not-first-port-of-call-for-managerial-recruits/2013863.article
Customer
retention
21. Traditional tools are unable to keep up with speed and velocity
of business demands, tools like redshift, tableau and alteryx
enable analysis within hours
Traditional BI : Keep calm and please wait Analysis at the speed of thought
V
S
We need tools that empower us to analyse at the speed of thought
23. Business intelligence stack
Source Data
IT
ELT/ET
L
Data
Warehouse
Business
ETL
Visualizer &
Reporting
• ECC
• CRM
• CSC
• Google Analytics
• Sales POS
• The list goes
on…
• Alteryx Desktop selected
& deployed
• Gives our business the
agility we need
• Business driven rather than
IT driven
• Ability to do predictive
analytics
• Tableau Desktop &
Server selected and
deployed
• Self serve BI
• Business dashboarding
• Ability to do predictive
analytics
Time to load
Reliability of Data
Query performance
Flexibility for slice/dice
Visualization performance
Data definitions / governance
Data exploration
• CDC used for
real time data
replication
• Slower to build
• More reliable
• Redshift selected
for reliability,
processing power
and scalability
24. Redshift provides the speed and robustness to store and analyze
vast volumes of data. Alteryx fuels the Tableau visualisation
allowing us to quickly gain insights in Tableau
Source
system
1
Livechat
Source
system
2
Zendesk
Amazon Redshift
Datamart
External data /
Marketing data
Blend & enrich Visualization
• Clean the data
• Apply business
rules
• Validate business
rules in teams
3
4
2
1
Comments
Tableau can be used to directly
visualise and analyse big data
directly from Redshift
1
Alteryx can be used to blend data
that isn't in the Redshift database
2
Alteryx can apply & validate more
complex business rules before
visualizing outputs in Tableau
Continuously iterate between
Alteryx and Tableau when
discovering more data
4
2
3
1
3
4
4
25. Alteryx enables us to combine different data sources in a visual
workflow before visualisation in Tableau
Server
Source systems Databases
Servers
Workstations
28. Democratize the opportunity
Source: http://www.heynataliejean.com/2009/01/end-of-nerd-era.html
Most business leaders want to use
data to make better decisions..
• You don’t have to be an IT
specialist to use Alteryx
• Give users access to the tools,
use Alteryx & Tableau to show
them how much faster you can
spot a trend
• 1:1 Training sessions for different
stakeholders tailored to their
needs
29. Make it relevant to different stakeholders
Senior management
• More interested in insights, dashboards and outcomes
• Spend time showing them dashboards built and how opportunities to improve
business performance (eg: revenue generation, expense reduction).
Functional specialists & leaders
• Alteryx relevant to functional leaders
• Alteryx to “clean” the data before visualizing in Tableau
• How can it be used to streamline processes
30. Celebrate success : Keep building and Iterating
Existing tools require analysts to have
“coding” experience
Few disparate analysts using SQL to
deliver analytical solutions
Where we are now
Using ‘Best’ of Breed products
• Visualization : Tableau
• Data blending: Alteryx
• Warehousing : Redshift (WIP)
Using Alteryx & Tableau to deliver :
• Revenue assurance
• Sim Sales
• Customer disconnections &
churn
• Port-outs by carrier
• CDR analysis
Where we will be
Self serve analytics for all business
users
• Real time P&L with Slice and
dice with visualization
• Assurance over logic
processing (Audit,
Commissions)
Advanced predictive analytics
• Churn propensity
• Customer behavior
Where we started
Traditional data integration is dominated by a few large, legacy vendors. This IT-driven, operational ETL model promised to create a single version of the truth inside a data warehouse so that was the only place a data analyst ever had to go to get the data they need. But, our research and industry reports suggest this model only succeeded in a small fraction of companies. Buying for operational ETL today is only from companies who are trying to maintain this model at all costs – hence the slow growth of this segment.
On the left we see a tiny market for data scientists who use complex programming languages like Perl, Python, and Pandas for data integration. These are powerful programming environments, but their steep learning curves inhibit use by data analysts who don’t have the time, or need, to invest in learning them.
And finally, on the right you see the rapidly growing data prep and blending market led by Alteryx. Gartner correctly called out this market as the place where the majority of spending for business user data integration is taking place. Data analysts appreciate the ability of Alteryx to allow them to prep and blend whatever data they want to use in their analysis – not what another department thinks they need. And do it with an intuitive user interface that doesn’t require any coding. Once the data is prepped and blended, Alteryx users can use the same workflow for analytics – predictive, statistical, and spatial – and deliver deeper insights in hours, not the weeks that are required with the other approaches.
For those unfamiliar with Amazon Redshift, it is a fast, fully managed, petabyte-scale data warehouse for less than $1000 per terabyte per year.
fast, cost effective, easy to use (launch cluster in a few minutes, scale with the push of a button)
Redshift is not only cheaper but also easy to use. Provisioning takes 15 minutes.
Amaysim is Australia’s award winning low cost mobile provider operating on the Optus network.
We are dedicated to delivering simplicity, fairness and low prices
Currently amaysim is one of the largest mobile virtual network operator (MVNO) with over 700,000 customers and one of the few telcos in Australia with a Net promoter score in mid 50-60s.
In additional we have also won money magazine's best of best awards for 2012 and 2013
As for me, my name is Adrian and I am the BI manager for amaysim. I’m responsible for the analytics strategy and execution across finance, retail sales, marketing and HR. My expertise includes decision support and consulting and visualization.
At amaysim had a bold vision for BI.. We want to deliver insight which enables business leaders and users of all levels to make better data driven decisions in realtime
It’s not about the size of data you have ; rather its about the ability to deliver continuous insight in a timely fashion.
AWS, Alteryx and Tableau enable us to ask and answer questions in that continuous iterative flow which is so important.
Every question leads to more questions. Delivering a dashboard is not the end rather it is the beginning with our Alteryx workflows and tableau dashboards continually being refined daily to help answer more questions.
Most importantly we use AWS, Alteryx and Tableau to provide recommendations to improve business performance…
We believe that the tools we have combined with our agility is what gives us that competitive edge
Many of you will ask ? Why have an analytics program. Well we have seen benefits in 3 mains areas.
1. Workforce Productivity
We have a 3 person analytics team covering wide span of functions (Finance, Marketing – Customer retention & acqusition, Sales – Retail and Online, HR)
By enabling line of business users to quickly build on a baseline of analytics, they can easily solve their own specific business problems quickly and do not have to wait on Business intelligence teams
2. Reduced time to insight
With alteryx, instead of gathering requirements, we instead ask a question on what is the business problem we are trying to solve.
Alteryx enables us to get to the data we need faster. Projects that would have taken 2-3 weeks are now down to a day
3. Data driven decision making
Line of business users are able to get direct access to their own data in an easy visualization enabling them to able to solve problems faster.
People can look at the data, do some discovery, and then arrive at an answer
Amaysim has a large variety of data sources, some of which have complicated data structures like XML and JSON
We ingest over 20m rows of data, each time a phone call, SMS or internet traffic is sent a row of data is generated
We exist in a competitive landscape where we compete for business against the 3 major carriers as well as a host of Mobile virtual network operators
In addition, we only have a small team of 3 analysts servicing a wide variety of audience
We exist in a competitive landscape where we compete for business against the 3 major carriers as well as a host of Mobile virtual network operators
So the question is why do we need both IT ETL and data blending capabilities?
The answer is really the speed and agility that alteryx gives us, it allows us to answer questions in minutes and hours rather than days of writing requirements.
It enables anyone to be an analyst and is not limited to just the BI team; in doing so we lift the productivity of the entire company by enabling line of business analysts to ask and answer their own questions
At amaysim we have adopted a best of breed solutions within BI from datawarehousing (redshift) to data blending (alteryx) to visualisation (tableau).
Like many companies, we have many different data sources, both internal and external which the business needs to analyse at short notice and act on.
Alteryx is a great tool which allows non-technical people to clean the data, apply complex rules, validate rules with stakeholders in an iterative manner before fuelling the tableau visualisation.
Redshift which is an massively parallel database in the cloud gives us the speed and robustness to store and analyse vast volumes of data.
Finally we use both Tableau desktop to slice and dice clean data and tableau server to really drive collboaration amongst business leaders.
Here’s another look on our setup of how we pull in multiple data sources including complex XML and JSON combine it with data in our database using Alteryx before visualisation in Tableau
Finally we use both Tableau server and Tableau desktop as a collaboration mechanism at Amaysim to deliver a variety of dashboards, data sources and insights to our business leaders
Our internal tableau server has a few key areas
Exec dashboard
Finance reporting
Marketing reporting
Customer service reporting
Retail sales
IT operations
I’ve covered the few areas on how tableau can be used with other best of breed products to enable business success.
So how do you bring about cultural change in a business?
This slide says it all.. How many business leaders look like that ??
Analytics is for everyone, not just IT or BI specialists!
Remember that You don’t have to be an IT specialist to use tools like Alteryx and Tableau!
Democraticise the opportunity..
Most business leaders want to combine analytics with industry experience to make better decisions.
Often the best way to show how alteryx & tableau works is to sit down 1 on 1 with a business leader and show them how alteryx can used to combine data together before visualising in Tableau.
You can often build a dashboard in under 10 minutes!
That way you can test your ideas in a dynamic flowing manner
Another important point to note when enabling business leaders is to understand and make tableau relevant for different stakeholders..
A person in senior management often has different needs to a functional specialist or leader
Senior management who are more interested in insights and how it can be used drive improved P&L performance.
Spend time with them understanding their needs and discuss if insights gleaned from tableau can result in improved revenue performance or expense reduction. (eg: revenue generation, expense reduction).
Functional leaders however may be more interested in a narrower field for example how they can spot trends which improve operational performance eg: HR leaders want to see metrics around staff turnover, absenteeism, leave accruals
Frontline leaders want to see metrics around how many calls are handled per hour, # livechats closed
Sales may want to see metrics around how many sales occur in a particular region or store.
The 3rd key ingredient of cultural change is to celebrate success.
At amaysim where we started, business intelligence was certainly not scalable as it required analysts to have “coding” experience.
There were a few disparate analysts using SQL to deliver analytical solutions. This way was certainly not scalable as business users were not empowered to analyze their business.
Where we are today, we are using Alteryx & Tableau to deliver insights and not just dashboards to management and are proactively trying to find ways to improve financial performance.
Each time we use Alteryx and Tableau to produce a new dashboard gets built, we don’t just send senior management a link, instead we take time to sit down one on one with management to explain what we are seeing in the dashboard and any trends (both positive and negative) which can be used to improve business performance..
This personalised approach is a key ingredient to success we have had.
We often gather a group of cross functional leaders for half an hour to click through and see if we can find any trends..
Where we will be is to have self serve analytics for all business users.
Realtime P&L with slice and dice capabilitites using Tableau data sources.
We will be using advanced predictive analytics to model and mitigate churn analysing customer behaviour and helping them find best plans to suit their needs.
I hope that this presentation will inspire you to build a culture of creative analytics which is both empowering and satisfying and brings out the best in people.
Democratize the Opportunity - Gather people of interest together from Sales, product, marketing
Make it relevant to different stakeholders - Include everyone & break down silos
Celebrate success - Keep people involved and excited