AWS Big Data Blog

Category: Analytics

Amazon Redshift Engineering’s Advanced Table Design Playbook: Table Data Durability

Part 1: Preamble, Prerequisites, and Prioritization Part 2: Distribution Styles and Distribution Keys Part 3: Compound and Interleaved Sort Keys Part 4: Compression Encodings Part 5: Table Data Durability (Translated into Japanese) In the fifth and final installment of the Advanced Table Design Playbook, I’ll discuss how to use two simple table durability properties to […]

Amazon Redshift Engineering’s Advanced Table Design Playbook: Compression Encodings

Part 1: Preamble, Prerequisites, and Prioritization Part 2: Distribution Styles and Distribution Keys Part 3: Compound and Interleaved Sort Keys Part 4: Compression Encodings (Translated into Japanese) Part 5: Table Data Durability In part 4 of this blog series, I’ll be discussing when and when not to apply column encoding for compression, methods for determining ideal […]

Amazon Redshift Engineering’s Advanced Table Design Playbook: Compound and Interleaved Sort Keys

  Part 1: Preamble, Prerequisites, and Prioritization Part 2: Distribution Styles and Distribution Keys Part 3: Compound and Interleaved Sort Keys (Translated into Japanese) Part 4: Compression Encodings Part 5: Table Data Durability In this installment, I’ll cover different sort key options, when to use sort keys, and how to identify the most optimal sort key […]

Amazon Redshift Engineering’s Advanced Table Design Playbook: Distribution Styles and Distribution Keys

  Part 1: Preamble, Prerequisites, and Prioritization Part 2: Distribution Styles and Distribution Keys (Translated into Japanese) Part 3: Compound and Interleaved Sort Keys Part 4: Compression Encodings Part 5: Table Data Durability The first table and column properties we discuss in this blog series are table distribution styles (DISTSTYLE) and distribution keys (DISTKEY). This blog […]

Amazon Redshift Engineering’s Advanced Table Design Playbook: Preamble, Prerequisites, and Prioritization

  Part 1: Preamble, Prerequisites, and Prioritization (Translated into Japanese) Part 2: Distribution Styles and Distribution Keys Part 3: Compound and Interleaved Sort Keys Part 4: Compression Encodings Part 5: Table Data Durability Amazon Redshift is a fully managed, petabyte scale, massively parallel data warehouse that offers simple operations and high performance. AWS customers use Amazon […]

Implementing Authorization and Auditing using Apache Ranger on Amazon EMR

Updated 3/30/2022: Amazon EMR has announced official support of Apache Ranger (link). Open-source plugin support will not be maintained moving forward and compatibility with latest versions will not be tested. We recommend customers to move to the Amazon EMR support for Apache Ranger. Ranger Presto plugin support on EMR has been deprecated. Updated 12/03/2020: Support for […]

Analyzing Data in S3 using Amazon Athena

April 2024: This post was reviewed for accuracy. Amazon Athena is an interactive query service that makes it easy to analyze data directly from Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to set up or manage and you can start analyzing your data immediately. You don’t even need to […]

Low-Latency Access on Trillions of Records: FINRA’s Architecture Using Apache HBase on Amazon EMR with Amazon S3

John Hitchingham is Director of Performance Engineering at FINRA The Financial Industry Regulatory Authority (FINRA) is a private sector regulator responsible for analyzing 99% of the equities and 65% of the option activity in the US. In order to look for fraud, market manipulation, insider trading, and abuse, FINRA’s technology group has developed a robust […]

Dynamically Scale Applications on Amazon EMR with Auto Scaling

Jonathan Fritz is a Senior Product Manager for Amazon EMR Customers running Apache Spark, Presto, and the Apache Hadoop ecosystem take advantage of Amazon EMR’s elasticity to save costs by terminating clusters after workflows are complete and resizing clusters with low-cost Amazon EC2 Spot Instances. For instance, customers can create clusters for daily ETL or machine learning […]