Social Network Analysis using Oracle Big Data Spatial & Graph (incl. why I di...Mark Rittman
As presented at OGh SQL Celebration Day 2016 - including new content on why NoSQL and Hadoop is a better solution for social network analysis than the Oracle Database (for now...)
Gluent New World #02 - SQL-on-Hadoop : A bit of History, Current State-of-the...Mark Rittman
Hadoop and NoSQL platforms initially focused on Java developers and slow but massively-scalable MapReduce jobs as an alternative to high-end but limited-scale analytics RDBMS engines. Apache Hive opened-up Hadoop to non-programmers by adding a SQL query engine and relational-style metadata layered over raw HDFS storage, and since then open-source initiatives such as Hive Stinger, Cloudera Impala and Apache Drill along with proprietary solutions from closed-source vendors have extended SQL-on-Hadoop’s capabilities into areas such as low-latency ad-hoc queries, ACID-compliant transactions and schema-less data discovery – at massive scale and with compelling economics.
In this session we’ll focus on technical foundations around SQL-on-Hadoop, first reviewing the basic platform Apache Hive provides and then looking in more detail at how ad-hoc querying, ACID-compliant transactions and data discovery engines work along with more specialised underlying storage that each now work best with – and we’ll take a look to the future to see how SQL querying, data integration and analytics are likely to come together in the next five years to make Hadoop the default platform running mixed old-world/new-world analytics workloads.
Using Oracle Big Data Discovey as a Data Scientist's ToolkitMark Rittman
As delivered at Trivadis Tech Event 2016 - how Big Data Discovery along with Python and pySpark was used to build predictive analytics models against wearables and smart home data
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Wareh...Mark Rittman
As presented at OGh SQL Celebration Day in June 2016, NL. Covers new features in Big Data SQL including storage indexes, storage handlers and ability to install + license on commodity hardware
Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive AnalyticsMark Rittman
This is a session for Oracle DBAs and devs that looks at the cutting edge big data techs like Spark, Kafka etc, and through demos shows how Hadoop is now a a real-time platform for fast analytics, data integration and predictive modeling
SQL-on-Hadoop for Analytics + BI: What Are My Options, What's the Future?Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we’ll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete “data fabric” solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Social Network Analysis using Oracle Big Data Spatial & Graph (incl. why I di...Mark Rittman
As presented at OGh SQL Celebration Day 2016 - including new content on why NoSQL and Hadoop is a better solution for social network analysis than the Oracle Database (for now...)
Gluent New World #02 - SQL-on-Hadoop : A bit of History, Current State-of-the...Mark Rittman
Hadoop and NoSQL platforms initially focused on Java developers and slow but massively-scalable MapReduce jobs as an alternative to high-end but limited-scale analytics RDBMS engines. Apache Hive opened-up Hadoop to non-programmers by adding a SQL query engine and relational-style metadata layered over raw HDFS storage, and since then open-source initiatives such as Hive Stinger, Cloudera Impala and Apache Drill along with proprietary solutions from closed-source vendors have extended SQL-on-Hadoop’s capabilities into areas such as low-latency ad-hoc queries, ACID-compliant transactions and schema-less data discovery – at massive scale and with compelling economics.
In this session we’ll focus on technical foundations around SQL-on-Hadoop, first reviewing the basic platform Apache Hive provides and then looking in more detail at how ad-hoc querying, ACID-compliant transactions and data discovery engines work along with more specialised underlying storage that each now work best with – and we’ll take a look to the future to see how SQL querying, data integration and analytics are likely to come together in the next five years to make Hadoop the default platform running mixed old-world/new-world analytics workloads.
Using Oracle Big Data Discovey as a Data Scientist's ToolkitMark Rittman
As delivered at Trivadis Tech Event 2016 - how Big Data Discovery along with Python and pySpark was used to build predictive analytics models against wearables and smart home data
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Wareh...Mark Rittman
As presented at OGh SQL Celebration Day in June 2016, NL. Covers new features in Big Data SQL including storage indexes, storage handlers and ability to install + license on commodity hardware
Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive AnalyticsMark Rittman
This is a session for Oracle DBAs and devs that looks at the cutting edge big data techs like Spark, Kafka etc, and through demos shows how Hadoop is now a a real-time platform for fast analytics, data integration and predictive modeling
SQL-on-Hadoop for Analytics + BI: What Are My Options, What's the Future?Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we’ll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete “data fabric” solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Riga dev day 2016 adding a data reservoir and oracle bdd to extend your ora...Mark Rittman
This talk focus is on what a data reservoir is, how it related to the RDBMS DW, and how Big Data Discovery provides access to it to business and BI users
Oracle BI Hybrid BI : Mode 1 + Mode 2, Cloud + On-Premise Business AnalyticsMark Rittman
Presented at the UKOUG Business Analytics SIG Meeting in April 2016, addresses the question as to whether enterprise BI tools such as OBIEE12c are relevant in the world of Gartner BiModal Mode 1 + Mode 2 analytics, and Hybrid cloud/on-premise deployments
Enkitec E4 Barcelona : SQL and Data Integration Futures on Hadoop : Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products such as Oracle Big Data SQL on the Oracle Big Data Appliance along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we'll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete "data fabric" solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
A series of tweets I posted about my 11hr struggle to make a cup of tea with my WiFi kettle ended-up going viral, got picked-up by the national and then international press, and led to thousands of retweets, comments and references in the media. In this session we’ll take the data I recorded on this Twitter activity over the period and use Oracle Big Data Graph and Spatial to understand what caused the breakout and the tweet going viral, who were the key influencers and connectors, and how the tweet spread over time and over geography from my original series of posts in Hove, England.
New World Hadoop Architectures (& What Problems They Really Solve) for Oracle...Rittman Analytics
Most DBAs are aware something interesting is going on with big data and the Hadoop product ecosystem that underpins it, but aren't so clear about what each component in the stack does, what problem each part solves and why those problems couldn't be solved using the old approach. We'll look at where it's all going with the advent of Spark and machine learning, what's happening with ETL, metadata and analytics on this platform ... why IaaS and datawarehousing-as-a-service will have such a big impact, sooner than you think
Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3.
Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32
There is a fundamental shift underway in IT to include open, software defined, distributed systems like Hadoop. As a result, every Oracle professional should strive to learn these new technologies or risk being left behind. This session is designed specifically for Oracle database professionals so they can better understand SQL on Hadoop and the benefits it brings to the enterprise. Attendees will see how SQL on Hadoop compares to Oracle in areas such as data storage, data ingestion, and SQL processing. Various live demos will provide attendees with a first-hand look at these new world technologies. Presented at Collaborate 18.
Big Data 2.0: ETL & Analytics: Implementing a next generation platformCaserta
In our most recent Big Data Warehousing Meetup, we learned about transitioning from Big Data 1.0 with Hadoop 1.x with nascent technologies to the advent of Hadoop 2.x with YARN to enable distributed ETL, SQL and Analytics solutions. Caserta Concepts Chief Architect Elliott Cordo and an Actian Engineer covered the complete data value chain of an Enterprise-ready platform including data connectivity, collection, preparation, optimization and analytics with end user access.
Access additional slides from this meetup here:
http://www.slideshare.net/CasertaConcepts/big-data-warehousing-meetup-january-20
For more information on our services or upcoming events, please visit http://www.actian.com/ or http://www.casertaconcepts.com/.
Many organizations focus on the licensing cost of Hadoop when considering migrating to a cloud platform. But other costs should be considered, as well as the biggest impact, which is the benefit of having a modern analytics platform that can handle all of your use cases. This session will cover lessons learned in assisting hundreds of companies to migrate from Hadoop to Databricks.
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
Hadoop is dead - long live Hadoop | BiDaTA 2013 Genoalarsgeorge
Keynote during BiDaTA 2013 in Genoa, a special track of the ADBIS 2013 conference. URL: http://dbdmg.polito.it/bidata2013/index.php/keynote-presentation
RDX Insights Presentation - Microsoft Business IntelligenceChristopher Foot
May's RDX Insights Series Presentation focuses on Microsoft's BI products. We begin with an overview of Power BI, SSIS, SSAS and SSRS and how the products integrate with each other. The webinar continues with a detailed discussion on how to use Power BI to capture, model, transform, analyze and visualize key business metrics. We’ll finish with a Power BI demo highlighting some of its most beneficial and interesting features.
Lambda architecture for real time big dataTrieu Nguyen
Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
Why lambda architecture is correct solution for big data?
Jeremy Engle's slides from Redshift / Big Data meetup on July 13, 2017AWS Chicago
"Strategies for supporting near real time analytics, OLAP, and interactive data exploration" - Dr. Jeremy Engle, Engineering Manager Data Team at Jellyvision
Building a Next-gen Data Platform and Leveraging the OSS Ecosystem for Easy W...StampedeCon
This session will be a detailed recount of the design, implementation, and launch of the next-generation Shutterstock Data Platform, with strong emphasis on conveying clear, understandable learnings that can be transferred to your own organizations and projects. This platform was architected around the prevailing use of Kafka as a highly-scalable central data hub for shipping data across your organization in batch or streaming fashion. It also relies heavily on Avro as a serialization format and a global schema registry to provide structure that greatly improves quality and usability of our data sets, while also allowing the flexibility to evolve schemas and maintain backwards compatibility.
As a company, Shutterstock has always focused heavily on leveraging open source technologies in developing its products and infrastructure, and open source has been a driving force in big data more so than almost any other software sub-sector. With this plethora of constantly evolving data technologies, it can be a daunting task to select the right tool for your problem. We will discuss our approach for choosing specific existing technologies and when we made decisions to invest time in home-grown components and solutions.
We will cover advantages and the engineering process of developing language-agnostic APIs for publishing to and consuming from the data platform. These APIs can power some very interesting streaming analytics solutions that are easily accessible to teams across our engineering organization.
We will also discuss some of the massive advantages a global schema for your data provides for downstream ETL and data analytics. ETL into Hadoop and creation and maintenance of Hive databases and tables becomes much more reliable and easily automated with historically compatible schemas. To complement this schema-based approach, we will cover results of performance testing various file formats and compression schemes in Hadoop and Hive, the massive performance benefits you can gain in analytical workloads by leveraging highly optimized columnar file formats such as ORC and Parquet, and how you can use good old fashioned Hive as a tool for easily and efficiently converting exiting datasets into these formats.
Finally, we will cover lessons learned in launching this platform across our organization, future improvements and further design, and the need for data engineers to understand and speak the languages of data scientists and web, infrastructure, and network engineers.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Deploying Full Oracle BI Platforms to Oracle Cloud - OOW2015Mark Rittman
Presentation given at Oracle Openworld 2015 on moving an existing OBIEE11g BI platform to Oracle Public Cloud, including accompanying DW database and continuing the ETL process. Explores migration process and what's now possible in Oracle Cloud for hosting full OBIEE platforms, and looks at what the benefits of such a migration might be for customers and end-users.
Riga dev day 2016 adding a data reservoir and oracle bdd to extend your ora...Mark Rittman
This talk focus is on what a data reservoir is, how it related to the RDBMS DW, and how Big Data Discovery provides access to it to business and BI users
Oracle BI Hybrid BI : Mode 1 + Mode 2, Cloud + On-Premise Business AnalyticsMark Rittman
Presented at the UKOUG Business Analytics SIG Meeting in April 2016, addresses the question as to whether enterprise BI tools such as OBIEE12c are relevant in the world of Gartner BiModal Mode 1 + Mode 2 analytics, and Hybrid cloud/on-premise deployments
Enkitec E4 Barcelona : SQL and Data Integration Futures on Hadoop : Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products such as Oracle Big Data SQL on the Oracle Big Data Appliance along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we'll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete "data fabric" solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
A series of tweets I posted about my 11hr struggle to make a cup of tea with my WiFi kettle ended-up going viral, got picked-up by the national and then international press, and led to thousands of retweets, comments and references in the media. In this session we’ll take the data I recorded on this Twitter activity over the period and use Oracle Big Data Graph and Spatial to understand what caused the breakout and the tweet going viral, who were the key influencers and connectors, and how the tweet spread over time and over geography from my original series of posts in Hove, England.
New World Hadoop Architectures (& What Problems They Really Solve) for Oracle...Rittman Analytics
Most DBAs are aware something interesting is going on with big data and the Hadoop product ecosystem that underpins it, but aren't so clear about what each component in the stack does, what problem each part solves and why those problems couldn't be solved using the old approach. We'll look at where it's all going with the advent of Spark and machine learning, what's happening with ETL, metadata and analytics on this platform ... why IaaS and datawarehousing-as-a-service will have such a big impact, sooner than you think
Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3.
Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32
There is a fundamental shift underway in IT to include open, software defined, distributed systems like Hadoop. As a result, every Oracle professional should strive to learn these new technologies or risk being left behind. This session is designed specifically for Oracle database professionals so they can better understand SQL on Hadoop and the benefits it brings to the enterprise. Attendees will see how SQL on Hadoop compares to Oracle in areas such as data storage, data ingestion, and SQL processing. Various live demos will provide attendees with a first-hand look at these new world technologies. Presented at Collaborate 18.
Big Data 2.0: ETL & Analytics: Implementing a next generation platformCaserta
In our most recent Big Data Warehousing Meetup, we learned about transitioning from Big Data 1.0 with Hadoop 1.x with nascent technologies to the advent of Hadoop 2.x with YARN to enable distributed ETL, SQL and Analytics solutions. Caserta Concepts Chief Architect Elliott Cordo and an Actian Engineer covered the complete data value chain of an Enterprise-ready platform including data connectivity, collection, preparation, optimization and analytics with end user access.
Access additional slides from this meetup here:
http://www.slideshare.net/CasertaConcepts/big-data-warehousing-meetup-january-20
For more information on our services or upcoming events, please visit http://www.actian.com/ or http://www.casertaconcepts.com/.
Many organizations focus on the licensing cost of Hadoop when considering migrating to a cloud platform. But other costs should be considered, as well as the biggest impact, which is the benefit of having a modern analytics platform that can handle all of your use cases. This session will cover lessons learned in assisting hundreds of companies to migrate from Hadoop to Databricks.
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
Hadoop is dead - long live Hadoop | BiDaTA 2013 Genoalarsgeorge
Keynote during BiDaTA 2013 in Genoa, a special track of the ADBIS 2013 conference. URL: http://dbdmg.polito.it/bidata2013/index.php/keynote-presentation
RDX Insights Presentation - Microsoft Business IntelligenceChristopher Foot
May's RDX Insights Series Presentation focuses on Microsoft's BI products. We begin with an overview of Power BI, SSIS, SSAS and SSRS and how the products integrate with each other. The webinar continues with a detailed discussion on how to use Power BI to capture, model, transform, analyze and visualize key business metrics. We’ll finish with a Power BI demo highlighting some of its most beneficial and interesting features.
Lambda architecture for real time big dataTrieu Nguyen
Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
Why lambda architecture is correct solution for big data?
Jeremy Engle's slides from Redshift / Big Data meetup on July 13, 2017AWS Chicago
"Strategies for supporting near real time analytics, OLAP, and interactive data exploration" - Dr. Jeremy Engle, Engineering Manager Data Team at Jellyvision
Building a Next-gen Data Platform and Leveraging the OSS Ecosystem for Easy W...StampedeCon
This session will be a detailed recount of the design, implementation, and launch of the next-generation Shutterstock Data Platform, with strong emphasis on conveying clear, understandable learnings that can be transferred to your own organizations and projects. This platform was architected around the prevailing use of Kafka as a highly-scalable central data hub for shipping data across your organization in batch or streaming fashion. It also relies heavily on Avro as a serialization format and a global schema registry to provide structure that greatly improves quality and usability of our data sets, while also allowing the flexibility to evolve schemas and maintain backwards compatibility.
As a company, Shutterstock has always focused heavily on leveraging open source technologies in developing its products and infrastructure, and open source has been a driving force in big data more so than almost any other software sub-sector. With this plethora of constantly evolving data technologies, it can be a daunting task to select the right tool for your problem. We will discuss our approach for choosing specific existing technologies and when we made decisions to invest time in home-grown components and solutions.
We will cover advantages and the engineering process of developing language-agnostic APIs for publishing to and consuming from the data platform. These APIs can power some very interesting streaming analytics solutions that are easily accessible to teams across our engineering organization.
We will also discuss some of the massive advantages a global schema for your data provides for downstream ETL and data analytics. ETL into Hadoop and creation and maintenance of Hive databases and tables becomes much more reliable and easily automated with historically compatible schemas. To complement this schema-based approach, we will cover results of performance testing various file formats and compression schemes in Hadoop and Hive, the massive performance benefits you can gain in analytical workloads by leveraging highly optimized columnar file formats such as ORC and Parquet, and how you can use good old fashioned Hive as a tool for easily and efficiently converting exiting datasets into these formats.
Finally, we will cover lessons learned in launching this platform across our organization, future improvements and further design, and the need for data engineers to understand and speak the languages of data scientists and web, infrastructure, and network engineers.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Deploying Full Oracle BI Platforms to Oracle Cloud - OOW2015Mark Rittman
Presentation given at Oracle Openworld 2015 on moving an existing OBIEE11g BI platform to Oracle Public Cloud, including accompanying DW database and continuing the ETL process. Explores migration process and what's now possible in Oracle Cloud for hosting full OBIEE platforms, and looks at what the benefits of such a migration might be for customers and end-users.
Lessons from Building Large-Scale, Multi-Cloud, SaaS Software at DatabricksDatabricks
The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software
Getting Into the Business Intelligence Game: Migrating OBIA to the CloudDatavail
This presentation discusses best practice architecture for migrating the Oracle BI Applications to the cloud. It focuses on the Oracle cloud platform and database services, with a nod to infrastructure services, to lay out the idea of the hybrid cloud, and variations of the new age cloud BI/DW architecture for your analytics environment to succeed while operating at the same reliability or better all the while benefiting from what the cloud offers best.
Practical Tips for Oracle Business Intelligence Applications 11g ImplementationsMichael Rainey
It's time to move to the Oracle Data Integrator version of Oracle Business Intelligence Applications! This has been the theme since it was recently announced that Oracle BI Applications 11g will move forward without updating functionality in the Informatica version of the product. Implementing Oracle BI Applications can be quite a challenge, specifically with Oracle Data Integrator being the “new” ETL tool. This session will provide attendees with practical tips, based on real-world experience, to help them get started with their implementation. How and why to use Oracle GoldenGate, high availability considerations, disaster recovery setup, and other functional and design factors will be covered, enhancing the attendee's ability to make the best design decisions for their BI Applications project.
Presented at KScope15 & NWOUG 2015.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
Building and Deploying Large Scale SSRS using Lessons Learned from Customer D...Denny Lee
This is Lukasz Pawlowski and my 2007 SQL PASS Summit presentation on building and deploying large scale SSRS using lessons learned from customer deployments
Presentation by Mark Rittman, Technical Director, Rittman Mead, on ODI 11g features that support enterprise deployment and usage. Delivered at BIWA Summit 2013, January 2013.
Modernisation of BI Business Intelligence and Data Warehouse Solutions at Tra...David Pui
Modernisation of BI Business Intelligence and Data Warehouse Solutions at Tranz Rail.
David Pui's led the modernisation of the BI and Data Warehouse Solutions whilst he was the Senior Technologist at Tranz Rail.
ADV Slides: 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.
With Power BI you can bring your BI architecture to the next level.
Architecture it's very important topic in a business intelligence project, let's discover which are right questions and possible scenarios to integrate Power BI in an existing environment or to build a new one from scratch.
We'll talkabout how to choose the right Storage Modes, how to design a refreshing policy, how to use dataflows to decouple and to lift the transformation process on Cloud and more.
Holistics - The Perfect Companion for Amazon Redshift
Amazon Redshift is great for the storing and processing of your data. However, there is still a significant effort involved in managing your data pipeline and analytics infrastructure.
Holistics provide Amazon Redshift customers with an end-to-end user interface across their data imports, data transformation and data presentation to address the complexities and challenges of managing their data analytics infrastructure pipeline.
The Summer 2016 release of Informatica Cloud is packed with many new platform features including :
- Cloud Data Integration Hub that supports publish and subscribe integration patterns that automate and streamline integration across cloud and on-premise sources
- Innovative features like stateful time sensitive variables, and advanced data transformations like unions and sequences
- Intelligent and dynamic data masking of sensitive data to save development and QA time.
-Cloud B2B Gateway is the leading data exchange platform for enterprises and it’ partners and customers providing end-to-end data monitoring capabilities and support for highest level of data quality.
- Enhancements to native connectors for popular cloud applications like Workday, SAP Success Factors, Oracle, SugarCRM, MongoDB, Teradata Cloud, SAP Concur, Salesforce Financial Services Cloud
And much more!
Similar to OTN EMEA Tour 2016 : Deploying Full BI Platforms to Oracle Cloud (20)
OBIEE12c and Embedded Essbase 12c - An Initial Look at Query Acceleration Use...Mark Rittman
OBIEE12c comes with an updated version of Essbase that focuses entirely in this release on the query acceleration use-case. This presentation looks at this new release and explains how the new BI Accelerator Wizard manages the creation of Essbase cubes to accelerate OBIEE query performance
What is Big Data Discovery, and how it complements traditional business anal...Mark Rittman
Data Discovery is an analysis technique that complements traditional business analytics, and enables users to combine, explore and analyse disparate datasets to spot opportunities and patterns that lie hidden within your data. Oracle Big Data discovery takes this idea and applies it to your unstructured and big data datasets, giving users a way to catalogue, join and then analyse all types of data across your organization.
In this session we'll look at Oracle Big Data Discovery and how it provides a "visual face" to your big data initatives, and how it complements and extends the work that you currently do using business analytics tools.
Delivering the Data Factory, Data Reservoir and a Scalable Oracle Big Data Ar...Mark Rittman
Presentation from the Rittman Mead BI Forum 2015 masterclass, pt.2 of a two-part session that also covered creating the Discovery Lab. Goes through setting up Flume log + twitter feeds into CDH5 Hadoop using ODI12c Advanced Big Data Option, then looks at the use of OBIEE11g with Hive, Impala and Big Data SQL before finally using Oracle Big Data Discovery for faceted search and data mashup on-top of Hadoop
OBIEE11g Seminar by Mark Rittman for OU Expert Summit, Dubai 2015Mark Rittman
Slides from a two-day OBIEE11g seminar in Dubai, February 2015, at the Oracle University Expert Summit. Covers the following topics:
1. OBIEE 11g Overview & New Features
2. Adding Exalytics and In-Memory Analytics to OBIEE 11g
3. Source Control and Concurrent Development for OBIEE
4. No Silver Bullets - OBIEE 11g Performance in the Real World
5. Oracle BI Cloud Service Overview, Tips and Techniques
6. Moving to Oracle BI Applications 11g + ODI
7. Oracle Essbase and Oracle BI EE 11g Integration Tips and Techniques
8. OBIEE 11g and Predictive Analytics, Hadoop & Big Data
UKOUG Tech'14 Super Sunday : Deep-Dive into Big Data ETL with ODI12cMark Rittman
Slides from my 2hr session at the UKOUG Tech'14 Super Sunday event, covering Hadoop basics and use of Oracle Data Integrator 12c for ETL on the Hadoop platform. Also some coverage of Oracle big data product announcements from OOW2014.
Part 1 - Introduction to Hadoop and Big Data Technologies for Oracle BI & DW ...Mark Rittman
Delivered as a one-day seminar at the SIOUG and HROUG Oracle User Group Conferences, October 2014
In this presentation we cover some key Hadoop concepts including HDFS, MapReduce, Hive and NoSQL/HBase, with the focus on Oracle Big Data Appliance and Cloudera Distribution including Hadoop. We explain how data is stored on a Hadoop system and the high-level ways it is accessed and analysed, and outline Oracle’s products in this area including the Big Data Connectors, Oracle Big Data SQL, and Oracle Business Intelligence (OBI) and Oracle Data Integrator (ODI).
Part 4 - Hadoop Data Output and Reporting using OBIEE11gMark Rittman
Delivered as a one-day seminar at the SIOUG and HROUG Oracle User Group Conferences, October 2014.
Once insights and analysis have been produced within your Hadoop cluster by analysts and technical staff, it’s usually the case that you want to share the output with a wider audience in the organisation. Oracle Business Intelligence has connectivity to Hadoop through Apache Hive compatibility, and other Oracle tools such as Oracle Big Data Discovery and Big Data SQL can be used to visualise and publish Hadoop data. In this final session we’ll look at what’s involved in connecting these tools to your Hadoop environment, and also consider where data is optimally located when large amounts of Hadoop data need to be analysed alongside more traditional data warehouse datasets
Part 2 - Hadoop Data Loading using Hadoop Tools and ODI12cMark Rittman
Delivered as a one-day seminar at the SIOUG and HROUG Oracle User Group Conferences, October 2014.
There are many ways to ingest (load) data into a Hadoop cluster, from file copying using the Hadoop Filesystem (FS) shell through to real-time streaming using technologies such as Flume and Hadoop streaming. In this session we’ll take a high-level look at the data ingestion options for Hadoop, and then show how Oracle Data Integrator and Oracle GoldenGate leverage these technologies to load and process data within your Hadoop cluster. We’ll also consider the updated Oracle Information Management Reference Architecture and look at the best places to land and process your enterprise data, using Hadoop’s schema-on-read approach to hold low-value, low-density raw data, and then use the concept of a “data factory” to load and process your data into more traditional Oracle relational storage, where we hold high-density, high-value data.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
3. info@rittmanmead.com www.rittmanmead.com @rittmanmead
About the Speaker
• Mark Rittman, Oracle ACE Director, Oracle BI, DW & Big Data
• 14 Years Experience with Oracle Technology
• Regular columnist for Oracle Magazine
• Author of two Oracle Press Oracle BI books
- Oracle Business Intelligence Developers Guide
- Oracle Exalytics Revealed
• Writer for Rittman Mead Blog :
http://www.rittmanmead.com/blog
• Past Editor of Oracle Scene Magazine,
BIRT SIG Chair, ODTUG Board Member
• Co-founder and CTO for Rittman Mead
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Oracle BI Cloud Service - What Is It?
• Oracle Business Intelligence, re-imagined for the cloud
• Runs as part of Oracle Public Cloud, part of wider Oracle Platform-as-a-Service
• Pay monthly, min 10 users, rolling upgrades and new features
• Entirely thin-client, simplified administration
• Comes with single DB schema,
50GB storage
• Aimed at departmental use-cases
- Sharing data from a spreadsheet
- Team reporting
- Development sandboxes
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Oracle Database Schema Provided with BICS
•Each instance of BICS comes with Oracle Schema Cloud Service
•ApEx environment with 10GB storage attached
•Able to run PL/SQL packages (with 5m timeout)
•Either create tables, views etc from ApEX,
or use data uploader in BICS
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Click to launch
ApEX Home Page
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Built-in Application Express Database Developer Tool
• Full ApEx environment for application building, table creation, SQL queries
• Can be used to build supporting applications, administration screens for OBIEE
application
• Make use of PL/SQL functionality
- Data cleansing
- Call Web Service REST APIs
- More complex processing
and calculations
• DB Cloud Service dashboard has tools
for data and application exports
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Options for Uploading Data to BI Cloud Service (DB Cloud)
• Use ApEx front-end and tools within it (data upload, data modeller etc)
• Use SQL*Developer and SFTP data upload facility
• Use BI Cloud Service Data Uploader
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Oracle BICS Initial Use-Case : Departmental Reporting
• Oracle BI Cloud Service in initial, standalone form aimed at departmental
reporting
- 50GB storage in single schema, limited ETL access
- Single subject area and limited RPD features
• Other common use-cases include
- Development environments
- Spin-off data discovery sandboxes
- Dedicated SaaS reporting applications
• But … part of wider Oracle Cloud platform
- Can we use that to host a whole BI system?
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17. But can I run my whole BI+DW
platform on Oracle Cloud?
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Where Do I Put my Data Warehouse?
• I’d like to migrate to cloud, but my BI System uses a full Oracle DW database
- How do I host that in the cloud? And replicate it there in the first place?
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Oracle Cloud Can Host Full BI Platforms … Here’s How
• Host Full Oracle Data Warehouse in Oracle DBaaS
• Upload and run on-premise RPD including use of multiple databases
- Including on-premise through new WebLogic connector
• Run full ETL using ODI
• Use VA without 12c upgrade
• Still have partner support
for key stages + new
development …
- But take away IT hassles
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Wider Oracle Public Cloud PaaS Ecosystem
• Similar to other public cloud services (AWS, Azure etc) at IaaS layer
• But with platform (PaaS) and software (SaaS) layers as differentiator
• Can we use Database, Storage and other layers to host full OBIEE platform?
- And what about ETL, identity management, file storage and so on?
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Hosting Full OBIEE Platforms in Oracle Public Cloud
• Leverages BICS’s new “Upload RPD Data
Models to the Cloud” feature
- Migrate supporting DW to full Oracle DBaaS
- Update on-prem RPD to connect to DBaaS
- Upload RPD (+now catalog!) to BICS
• Create new Oracle Cloud users for BICS
• ETL can connect via SQL*Net, JDBC etc
• Use for wider use-cases than BICS incl.
- Host full production platform (or test, dev)
- Create development branches, etc
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Oracle BICS
Oracle DBaaS
RPD
Upload
ETL ToolsBI Administration On-Premise Source DB
Data Uploads
via SQL*Net
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BICS Upload RPD to Cloud Key Steps
1. Make sure on-premise data model is consistent, no errors etc
2. Create DBaas Instance, Upload on-premise database to cloud
3. Update RPD Connection Pool settings to point to DBaaS instance
4. Recreate any user accounts, application roles etc
5. Create new dashboards and reports
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Example Migration : Full OBIEE11g SampleApp v406
• OBIEE11g SampleApp v506 - based on 11.1.1.9 release and delivered on VM
• Comes with Oracle 12c Database with multiple schemas, MVs, partitioning etc
- Also uses Essbase, Hadoop etc but out of scope for this exercise
• Objective is to migrate OBIEE, DW database and security elements
- Verify that ODI can still connect and load DW in Oracle Cloud
- Look at options for additional DB sources,
Essbase, Hadoop sources etc
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Creating a Full Database Instance in Oracle DBaaS
• Initial step is to create an Oracle Database Cloud Service Instance
• Options for 11g or 12c Release Database, High / Extreme Performance Options
- Determines scope of DB features available (OLAP, partitioning etc)
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Oracle Database Cloud Service Connectivity / Configuration
• Database runs in a VM, has Listener and other processes running
• Database parameters can be altered, but DB Options set by DBaaS version
- OLAP, Partitioning Options not present may affect DB uploads
• Ports blocked by default, need to be opened for SQL*Net etc access
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Managing and Monitoring Database Cloud Service
• Oracle Enterprise Manager Database Express 12c for DBA tasks
- Features determined by DBaaS edition
(regular, High or Extreme Performance)
• Virtualised Cloud Hosting monitored by
Oracle DBaaS Monitor
- RDBMS storage, alerts, processes etc
- OS-level monitoring
- Listener
- Backups etc
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Configuring On-Premise RPD to Connect to DBaaS
• Update Connection Pool settings in on-prem RPD before upload to BICS
- Note : DBaaS instance must be in same datacenter as BICS
• Note - No need to change overall DB connection setting in BICS Console
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Steps to Upload RPD to BI Cloud Service
• Backup (snapshot) current BICS environment if required
• Select Snapshots > Replace Data Model
• Use Browse button to select RPD from desktop, then upload to BICS
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Additional BICS Capabilities when using Uploaded RPD
• Allows multiple subject areas vs. single one in standard BICS
• Multiple DBaaS instances can be mapped into RPD for federated queries
• Full access to RPD features - vertical/horizontal federation etc
• Data Mashups also available (but not VA)
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Additional Post Upload Configuration : Mapping
• Mapping is now available in BICS, requires further Administrator configuration
• Single map provider (OracleMaps) with set of associated layers
• As with on-premise, map layers then need to be linked to subject area columns
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Configuring Security and Recreating Users, Roles
• On-premise users need to have corresponding new account created in BICS
- Also creates an Oracle Cloud login for BICS identity domain - separate to OTN login
• BICS licensed per user, min is 10 per pod with test and prod instances
• Application roles also need to be recreated and users added
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SSO between Oracle SaaS and BICS
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• Allows an Oracle Cloud customer the
ability to have SSO between different
Cloud Apps, e.g. BICS and PBCS
• Requirements:
- Services need to be ordered in
the same data center
- Customer must activate
services under the same
identity domain
• When in the same IDM, a single
user can be authorized for one or
more available services.
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Row-level Security Within RPD
• Row-level security works properly within BICS and uploaded RPDs
• Best practice is to base on application roles, recreate matching ones to proceed
• Regular and row-wise session variables then work as expected
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Limitations on BICS with RPD Upload vs On-Premise
• Can only access other DBaaS sources at present - see next slide
- Presume integration with PBCS, Oracle Big Data Cloud Service, SaaS apps to come
- No ability at present to access Essbase, TT or other acceleration layers
• No further editing of the uploaded RPD - but TCM enhancements soon
• Upload the catalog to go with RPD - coming soon
• Further limitations that may possibly be lifted in the future
- Adding of HTML or Javascript to Analyses is disabled
- Some limitations around alternate sorts and other small issues
- Visual Analyser not available yet (multiple subject area issue?)
- No usage tracking (but see MOS Doc.ID 1965207.1)
• No BI Publisher - though can install through JCS + DBaaS
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Accessing On-Premise Sources - Now Possible
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• On-Premise Data Access using WebLogic Connector
• Enables direct query to on-premises accessible databases
- Secure & Optimized for WAN transport
- Free download from OTN, requires WebLogic
- Phase 1: Requires Admin tool & RPD upload
- Phase 2: Thin Client Modeler support
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What About ETL and Data Integration?
• ODI12c (and other tools using SQL*Net, JDBC etc) can connect to DBaaS as
normal
• Either through SSH tunnel and SQL*Net,
or direct from internet if ports enabled
• Current data integration and ETL
can therefore continue as normal
• Makes it possible to create a
hybrid on-prem/cloud solution
On Premise
ODI
SaaS Apps
Oracle Data Integrator
ODI/EDQ
Java
Cloud Service
ODI
Big Data
Cloud Service
Database
Cloud Service
Exadata
Cloud Service
Storage
Cloud Service
Business Intelligence
Cloud Service
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Future Option : Oracle Big Data Preparation Cloud Service
• Oracle Cloud-based Data Preparation Service, aimed at data domain experts
• Takes files and other datasets from Oracle Cloud and prepares for analysis
• Split, transform and obfuscate data before loading into Hadoop data platform
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Uses Machine-Learning to Automate Data Recommendations
• Uses Spark MLlib machine learning to
profile data and recognise patterns
• Automates many of the routine data
preparation and profiling work
- Spot credit card, SSN + other sensitive
data, recommends masking
- Suggest appropriate names, datatypes
for columns based on
format and data patterns
• Allows analyst to focus on key tasks
- Example of cloud app consumerization
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Cloud Datasource Integration & File Upload/Download
• Primary datasource and target is Oracle Storage Cloud Service (like Amazon S3)
- Oracle Big Data Cloud Service, Oracle DBaaS and others
• User can also upload / download files directly into Big Data Prep Service
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How Can We use Big Data Prep Service with BICS?
• User uploads or downloads files (CSV,
TSV, Excel, Word, PDF, etc.) into Oracle
Storage Cloud Service
• Big Data Prep Service loads files
from Storage Cloud for preparation
• Prepared files are written back into
Storage Cloud Service
• User uploads prepared files into BICS
as datasource for ApEx / Schema
Service
- On roadmap : direct integration with VA
+ Answers for data-mashup data files
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Proposed Direct BDP Integration for VA / Data-Mashup Files
• Roadmap proposal from Oracle is to make BDP available from BICS homepage
• Load and prepare data to add as Visual Analyser & Answers Mashup data files
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Direct BICS / BDP Integration using Oracle Data Integrator
• Possible now : Use of ODI to prepare individual files through BDP
• ODI on-premise used to automate file extracts and upload into Storage CS using its
REST APIs
• BDP picks up files available in
Storage CS and prepares them
• Prepared files are written back
into Storage CS
• Prepped files downloaded by ODI
from Storage CS, loaded into BICS
- On roadmap: BDP will be able to
load prepared data directly into BICS
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Summary and Next Steps
• It’s now possible (mostly…) possible to migrate full OBIEE systems to cloud
• On-premise RPDs + catalog uploaded into BI Cloud Service
• Linked to DBaaS for DW, can access other DBaaS source + on-prem data
• ODI can still upload data from on-premise sources into DBaaS, or consider BDP
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