Build & Query Secure S3 Data Lakes with Ahana Cloud and AWS Lake Formation

In this webinar, we’ll share more on the recently announced AWS Lake Formation and Ahana integration. The AWS & Ahana product teams will cover:

Quick overview of AWS Lake Formation & Ahana Cloud
-The details of the integration
-How data platform teams can seamlessly integrate Presto natively with AWS Glue, AWS Lake Formation and AWS S3 through a demo

Transcript

1. Build and Secure S3 Data Lakes with Ahana Cloud and AWS Lake Formation January 11, 2021
2. Agenda 2 • AWS Lake Formation • Ahana • Ahana Cloud for Presto and AWS Lake Formation Integration
3. Agenda 3 • AWS Lake Formation • Ahana • Ahana Cloud for Presto and AWS Lake Formation Integration
4. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Companies want more value from their data Used by many people Growing exponentially From new sources Increasingly diverse Analyzed by many applications
5. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Building and securing data lakes can take months
6. AWS Lake Formation 6
7. What is AWS Lake Formation? 7 1. Building data lakes quickly. Days not months. Ability to move, store, update and catalog your data faster. Automatically organize and optimize your data. 2. Simplify security management. Centrally defining and enforcing security, governance, and auditing policies. 3. Making easy to discover and share data. Cataloging all of an organization's data assets and easily share datasets between consumers.
8. Agenda 8 • AWS Lake Formation • Ahana • Ahana Cloud for Presto and AWS Lake Formation Integration
9. Presto 9 1. Distributed query engine ANSI SQL on databases and data lakes. 2. Low latency performance on petabytes of data 3. Pluggable connector architecture Supports many data sources. Federated querying. 4. Open source Originally developed at Meta. Currently, under auspices of the Linux Foundation. Data Lakehouse analytics Reporting & dashboarding Interactive ad hoc querying Transformation using SQL (ETL) Federated querying across data sources
10. Presto Overview 10 Presto Cluster Coordinator Worker Worker Worker Worker
11. Managing Presto Remains Complex Hadoop complexity ▪ /etc/presto/config.properties ▪ /etc/presto/node.properties ▪ /etc/presto/jvm.config Many hidden parameters – difficult to tune Just the query engine ▪ No built-in catalog – users need to manage Hive metastore or AWS Glue ▪ No data lake S3 integration Poor out-of-box perf ▪ No tuning ▪ No high-performance indexing ▪ Basic optimizations for even for common queries
12. Ahana Cloud For Presto 12 1. Zero to Presto in 30 Minutes. Managed cloud service: No installation and configuration. 2. Built for data teams of all experience level. 3. Moderate level of control of deployment without complexity. 4. Dedicated support from Presto experts.
13. How Ahana Cloud Works ~ 30 mins to create the compute plane https://app.ahana.cloud/signup Create Presto Clusters in your account
14. Agenda 14 • AWS Lake Formation • Ahana • Ahana Cloud for Presto and AWS Lake Formation Integration
15. Benefits 15 Bring Presto to your existing AWS stack You’ve stored all your data in AWS S3 and may have already defined your data catalog resources and IAM roles with Glue and Lake Formation. Ahana Cloud for Presto can integrate natively with all these services. Enforce access controls in Presto down to row level Define access control policies through Lake Formation to protect confidentiality & handle sensitive data. Presto will honor these policies, returning only accessible data in queries or denying access altogether. Configure Lake Formation with Ahana in a few clicks Ahana Cloud for Presto streamlines AWS Lake Formation configuration and allows you to visually map your Presto users to IAM roles. You define a data source once and apply it to all your Presto clusters. Fine-Grained Access Control Easy to Use AWS Native
16. Data Lake on AWS S3 16
17. Single Click Enable 17
18. Easy Presto User to IAM Role Mapping 18 Presto User AWS IAM to Presto User Mapping
19. Fine-Grained Permissions 19 Column-level security Row-level security Table-level security Database-level security
20. DEMO
21. Sales Transactions 21 transactions transactions sales customers employees hr
22. Customers 22 transactions sales customers employees hr customers
23. Company Employees 23 transactions sales customers employees hr employees
24. 24 Annie Admin transactions sales customers employees hr
25. 25 Sally Sales Analyst transactions sales customers employees hr
26. 26 Sally Sales Analyst Transactions
27. 27 Wally Marketing Customers
28. 28 Olivia Marketing Customers
29. 29 Harry HR Employees
30. 30 Oscar Other transactions sales customers employees hr
31. Review 31 User Role Permission Fine-Grained Access Control Annie Admin All All Access Sally Sales Analyst transactions Table Database, Table, and Column Level Security Wally Marketing Analyst - WA customers Table Database, Table, Column and Row Level Security Olivia Marketing Analyst - OR customers Table Database, Table, Column and Row Level Security Harry HR Analyst All tables in HR database (employees) Database and Table Level Security Oscar None None No Access