How Yellowbrick Data Integrates to Existing Environments Webcast
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This document discusses how Yellowbrick can integrate into existing data environments. It describes Yellowbrick's data warehouse capabilities and how it compares to other solutions. The document recommends upgrading from single server databases or traditional MPP systems to Yellowbrick when data outgrows a single server or there are too many disparate systems. It also recommends moving from pre-configured or cloud-only systems to Yellowbrick to significantly reduce costs while improving query performance. The document concludes with a security demonstration using a netflow dataset.
![Retail analytics provider
We had six engineers touch the system and all of them found the system very
easy to use because there was a lot of commonality with the existing systems
that we already had.”
yellowbrick.com/symphony
14
“The [query] performance improvements we saw
were from 3x to 10x improvement basically
running them as is.](https://image.slidesharecdn.com/howyellowbrickdataintegratestoexistingenvironmentswebcast-190425181315/85/How-Yellowbrick-Data-Integrates-to-Existing-Environments-Webcast-14-320.jpg)
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How Yellowbrick Data Integrates to Existing Environments Webcast
- 1. Gary Orenstein Chief Product Officer April 2019 How Yellowbrick Integrates into Existing Environments
- 2. In today’s talk Market Drivers Yellowbrick Data Warehouse Overview and Landscape Data Management Evolution How to Introduce Yellowbrick to Existing Environments Security Application Demonstration 2
- 4. Demands on data organizations Serve more users, internally and externally Provide more analytic services simultaneously • New analytic applications • Ad hoc business intelligence • Operational dashboards • All driving need for fast loading and high performance Save money with deployment flexibility and consolidation 4
- 5. The Yellowbrick Data Warehouse
- 6. Yellowbrick fits enterprise needs Always on and available Ad-hoc SQL queries Correct answers on any schema Terabytes to petabytes of data Mixed real-time inserts, ETL, batch, interactive workloads Thousands of concurrent users 6
- 7. Yellowbrick Data Warehouse attributes Real-time Feeds Ingest IoT or OLTP data Capture 100,000s of rows per second Interactive Applications Serve short queries in under 100 milliseconds Periodic Bulk Loads Capture terabytes of data, petabytes over time Powerful Analytics Respond to complex ad hoc BI queries in just a few seconds Load and Transform Use existing ETL tools including intensive push-down ELT Business Critical Reporting Workload management for prioritized responses PostgreSQL compatible 7
- 8. The Yellowbrick Data Warehouse 8 PURPOSE-BUILT ALL FLASH SQL ENGINE From tens of terabytes to 2.4 petabytes MPP architecture Start small Grow compute and storage DEPLOY IN THE DATA CENTER OR CLOUD
- 9. Data warehousing approaches 9 SCALE UP SCALE OUT SCALE OUT with minimal resources Go as far as a single server can go Add servers continuously for scale (parallel systems) Maximum users, most amount of data, minimum resources Couple with workload management SQL Server Oracle IBM Netezza Greenplum Vertica Teradata Oracle Exadata SAP HANA Cloud-only AWS Redshift Snowflake Yellowbrick Data
- 11. Simplified evolution of data management Enterprise Data Warehouse model • Consolidate one or multiple application data sets into a data warehouse Desire to capture all Internet data led to adoption of a data lake • However, MapReduce was challenging SQL-as-a-Layer provides some relief • But SQL on a file system IS NOT a data warehouse SQL as a Layer 11
- 12. Incoming Data Structured and semi-structured The Yellowbrick Data Warehouse 1000s of users (Applications, BI analysts, Data engineers) High value data moves to EDW Unstructured data Data Lake Data science Modern architecture for scalable SQL analytics 12
- 14. Retail analytics provider We had six engineers touch the system and all of them found the system very easy to use because there was a lot of commonality with the existing systems that we already had.” yellowbrick.com/symphony 14 “The [query] performance improvements we saw were from 3x to 10x improvement basically running them as is.
- 15. Integrating with Yellowbrick Data Ingest - Directly from SQL applications - Via real-time streams from Kafka - Transformation with Spark Client interoperability with PostgreSQL Interactive Applications Build new analytical applications Bulk Loading Use YBLOAD to move flat files at 1 GB/s, and up to 6 TB/hr Powerful BI Analytics Respond to ad hoc BI queries from MicroStrategy, Tableau, Business Objects and more Load and Transform - Informatica, Attunity, Talend, Syncsort - Spark ETL Business Critical Reporting Build prioritized responses and multi-department support with workload management Yellowbrick Database Details ANSI SQL, stored procedures, views, schemas, workload management Data Mining SAS, R, Python 15
- 16. Data warehousing approaches 16 SCALE UP SCALE OUT SCALE OUT with minimal resources Go as far as a single server can go Add servers continuously for scale (parallel systems) Maximum users, most amount of data, minimum resources Couple with workload management SQL Server Oracle IBM Netezza Greenplum Vertica Teradata Oracle Exadata SAP HANA Cloud-only AWS Redshift Snowflake Yellowbrick DataTraditional MPP Preconfigured Cloud-only Single Server
- 17. Integrating (upgrading) single server databases When should you move? - Outgrown a single server - Managing too many independent databases 17 Benefits with Yellowbrick - More capacity (compute and historical data) - Simplified operation - Same well-known SQL models GROWTH BEYOND A SINGLE SERVER TOO MANY DISPARATE SYSTEMS YELLOWBRICK DATA WAREHOUSE
- 18. Integrating (upgrading) traditional MPP systems When should you move? - Netezza - NOW! Prepare for end-of support - Vertica/Greenplum - When you need more active data warehouse development - Teradata - When you are intent on saving millions of dollars and willing to make selective changes to your workflow 18 Benefits with Yellowbrick - For Netezza, Greenplum, Vertica users - Same PostreSQL! - For all traditional MPP users - Massive savings in legacy footprint while achieving 3-100x query improvements
- 19. Integrating (upgrading) ‘pre-configured’ systems When should you move? - When there is corporate objective to reduce infrastructure costs from Oracle or SAP - When there is understanding that core apps can remain on ‘pre-configured’ systems, and analytics can move to open, standards-based data warehouses - When you want to save big $$$ 19 Benefits with Yellowbrick - Significantly lower costs due to a flash-centric architecture instead of a DRAM-centric architecture - Ability to open analytic access to multiple departments and teams simultaneously without incurring excessive costs PRECONFIGURED SYSTEM
- 20. Integrating (upgrading) cloud-only systems When should you move? • When core your data and analytics engine has become an unpredictable and out-of-control cost • When you have reached a performance or concurrency limit with cloud-only solutions • When you prefer the control of hybrid environment, in the cloud with options for on-premises 20 Benefits with Yellowbrick - Redshift is also a PostgreSQL compatible datastore, making for easy migration - Yellowbrick offers cloud and on-premises options with predictable costs CLOUD-ONLY
- 22. DEMO Details Netflow dataset from Spanish ISP Intrusion detection system testing Network capture over a six month period Yellowbrick Showcase - Perfect solution with Tableau, MicroStrategy or any BI - Ideal solution for concurrent queries on fewer/smaller systems 22
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- 29. Customer videos at www.yellowbrick.com 29
- 31. THANK YOU yellowbrick.com S E E I N G I S B E L I E V I N G