How to Build an Open Data Lake Analytics Stack

Transcript

1. How to Build an Open Data Lake Analytics Stack Dipti Borkar Cofounder, Chief Product Officer & Chief Evangelist Chairperson |Community Team Presto Foundation
2. 2 Today’s Speaker Dipti is a Cofounder, CPO & Chief Evangelist of Ahana with over 15 years experience in distributed data and database technology including relational, NoSQL and federated systems. She is also the Presto Foundation Outreach Chairperson. Prior to Ahana, Dipti held executive roles at Alluxio, Kinetica and Couchbase. At Alluxio, she was Vice President of Products and at Couchbase she held several leadership positions there including VP, Product Marketing, Head of Global Technical Sales and Head of Product Management. Earlier in her career Dipti managed development teams at IBM DB2 Distributed where she started her career as a database software engineer. Dipti holds a M.S. in Computer Science from UC San Diego, and an MBA from the Haas School of Business at UC Berkeley. Dipti Borkar Cofounder, Chief Product Officer and Chief Evangelist Ahana
3. 3 Why Open Data Lake Analytics? Enterprise Data Beyond Enterprise Data IoT, Third-party, Telemetry, Event 1000X More Data Terabytes to Petabytes Open & Flexible Open Source, Open Formats Reporting & Dashboarding Data Science In-data lake transformation Reporting & Dashboarding Data Warehouse Open Data Lakes
4. 4 What is Open Data Lake Analytics
5. 5 The Traditional Data Warehouse • Relational Database • Columnar Structure • In-Database Analytics • Structured Data • Modeled Data • Extract, Transform, Load • SQL Access Challenges • Expensive • Difficult to Manage • Costly to Maintain • Limited Data • Limited Access 5
6. 6 The Drivers Behind Modernization Digital Transformation Real Time Events Modern Processing Techniques More Data Fast Data Smart Data The Deconstructed Database
7. 7 The Traditional Data Lake • File System Data Store / Object Store • Structured / Semi-Structured Data • Ingestion • Discovery • Data Science • Notebook and Python Access • Less expensive, but… • Good enough performance • Supports ~70% of DW workloads • Different approach to governance 7
8. 8 Data SQL Query Processing Data Warehouse Cloud Data Lake Data Processing 1-10 TB 1TB -> PB Open Data Lake Analytics Reporting & Dashboarding Data Science In-data lake transformation Open Data Lake Analytics Reporting & Dashboarding
9. 9 Data Warehouse Operational Data Stores Third Party Data Machine Learning Semi- | unstructured Data Virtualization / Federated Access Streaming & IoT Data SQL Query Processing SQL Query Processing The Data Platform ETL ELT Data Engg Storage Compute 1-10 TB Query & Processing Storage Compute SQL Structured Workloads 1TB -> PB Data Lake Reporting Dashboards Visualizations Notebooks Custom Apps
10. 10 Cloud data lake driving open source SQL query engines Presto is the De-Facto SQL Engine for Data Lakes https://db-engines.com/en/ranking_trend/relational+dbms
11. Merging the Data Warehouse and the Data Lake with a Distributed Query Engine 11 1. SQL Access 2. Data Lake and Data Warehouse Access 3. Unified Analytics 4. Distributed Queries 5. Limitless Scale 6. Complex Data Types • Leverage Resources • Better Insight • More Use Cases • Leverage Platforms • Remove Limits • Amplified Insight
12. Data Lake Use Cases
13. 13 Emerging use cases Use Cases Data Lakehouse analytics Reporting & dashboarding Interactive querying use cases Transformation using SQL (ETL) Federated access across data sources SQL Data Science Customer-facing app analytics
14. 14 The Data LakeHouse Components Dashboad / notebooks Compute / Query engine Operational catalog Table Format / Transaction manager Storage
15. 15 The Data LakeHouse Stack
16. Considerations for Open Analytics Decision © 2021 Enterprise Management Associates, Inc. 16 | @ema_research Data Analytics Users Platform Cloud Enterprise Business Cost
17. Considerations for Any Unified Analytics Decision Data Structured Semi-Struc tured Real Time Structured Complex Data Types Textual Streaming © 2021 Enterprise Management Associates, Inc. 17 | @ema_research
18. Considerations for Any Unified Analytics Decision Data Analytics Users Platform SQL Python Notebook Search © 2021 Enterprise Management Associates, Inc. 18 | @ema_research
19. Considerations for Any Unified Analytics Decision Data Analytics Users Platform Engineer Analyst Scientist Business © 2021 Enterprise Management Associates, Inc. 19 | @ema_research
20. Considerations for Any Unified Analytics Decision Data Analytics Users Platform Cloud Enterprise Business Cost
21. Considerations for Any Unified Analytics Decision Elasticity Scale Mobility Globality Cloud Enterprise Business Cost © 2021 Enterprise Management Associates, Inc. 21 | @ema_research
22. Considerations for Any Unified Analytics Decision Security Privacy Governance Unification Cloud Enterprise Business Cost © 2021 Enterprise Management Associates, Inc. 22 | @ema_research
23. Considerations for Any Unified Analytics Decision Semantics Logic Value Optimization Cloud Enterprise Business Cost © 2021 Enterprise Management Associates, Inc. 23 | @ema_research
24. Considerations for Any Unified Analytics Decision Forecast Containment Chargeback Scale Cloud Enterprise Business Cost © 2021 Enterprise Management Associates, Inc. 24 | @ema_research
25. 25 Challenges with SQL on Open Data Lakes Cloud DW / AWS Serverless options get very expensive for growing data volumes ▪ Cloud data warehouse costs grow much faster than compute engine costs ▪ Serverless options like AWS Athena charge /query and get expensive “Do it yourself” approach is complicated ▪ Big data skills in platform teams are limited ▪ Presto is complicated and operationally very time consuming Presto on AWS like AWS Athena has limited capabilities and doesn’t scale ▪ Limited concurrency of 20 per account ▪ No visibility into cluster logs, query logs, no flexibility / control on scale
26. Presto & Presto Community
27. 27 Open Source Presto Overview • Distributed SQL query engine • Created at • ANSI SQL on Databases, Data lakes • Designed to be interactive & access petabytes of data • Open source, hosted at https://github.com/prestodb
28. 28 Presto Users
29. Ahana Overview
30. 30 How Ahana Cloud works? ~ 30 mins to create the compute plane https://app.ahana.cloud/signup Create Presto Clusters in your account
31. 31 Ahana Cloud for Presto Ahana Console (Control Plane) CLUSTER ORCHESTRATION CONSOLIDATED LOGGING SECURITY & ACCESS BILLING & SUPPORT In-VPC Presto Clusters (Compute Plane) AD HOC CLUSTER 1 TEST CLUSTER 2 PROD CLUSTER N Glue S3 RDS Elasticsearch Ahana Cloud Account Ahana console oversees and manages every Presto cluster Customer Cloud Account In-VPC orchestration of Presto clusters, where metadata, monitoring, and data sources reside
32. 32 Ahana Cloud Overview 1. Ahana Managed Service Console 2. Add data sources 3. Query data where it lives with Federated Connectors (in place) 4. Cluster management
33. 33 Case study: Securonix NextGen SIEM Cluster AWS S3 Data Lake Glue Metastore ▪ Securonix is a Security information and event management software ▪ They use Ahana for in-app SQL analytics on data from AWS S3 for threat hunting ▪ They pull in billions of events per day that get stored in S3 ▪ With Ahana Cloud, they saw 3x better price performance compared with Presto on AWS
34. 34 Ahana Cloud for Presto - Summary ▪ Brings SQL on AWS S3 with an open data lake + USER ▪ Presto compute brought to your data in your VPC in your account ▪ Fully managed Presto cluster life cycle including idle-time management ▪ Query AWS DBs - RDS/MySQL , RDS/Postgres, Elasticsearch, Redshift, Elasticsearch ▪ Cloud-native and highly available running on Kubernetes ▪ Bring your own ▪ BI tool / Data Science Notebook ▪ Metadata Catalog ▪ Transaction Manager Easy to use 3 x Price Performance Open & Flexible
35. 12/17/20