AWS Big Data Blog

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Best practices to optimize cost and performance for AWS Glue streaming ETL jobs

AWS Glue streaming extract, transform, and load (ETL) jobs allow you to process and enrich vast amounts of incoming data from systems such as Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka (Amazon MSK), or any other Apache Kafka cluster. It uses the Spark Structured Streaming framework to perform data processing in near-real […]

How Epos Now modernized their data platform by building an end-to-end data lake with the AWS Data Lab

Epos Now provides point of sale and payment solutions to over 40,000 hospitality and retailers across 71 countries. Their mission is to help businesses of all sizes reach their full potential through the power of cloud technology, with solutions that are affordable, efficient, and accessible. Their solutions allow businesses to leverage actionable insights, manage their […]

How SumUp built a low-latency feature store using Amazon EMR and Amazon Keyspaces

This post was co-authored by Vadym Dolin, Data Architect at SumUp. In their own words, SumUp is a leading financial technology company, operating across 35 markets on three continents. SumUp helps small businesses be successful by enabling them to accept card payments in-store, in-app, and online, in a simple, secure, and cost-effective way. Today, SumUp […]

Stream Amazon EMR on EKS logs to third-party providers like Splunk, Amazon OpenSearch Service, or other log aggregators

Spark jobs running on Amazon EMR on EKS generate logs that are very useful in identifying issues with Spark processes and also as a way to see Spark outputs. You can access these logs from a variety of sources. On the Amazon EMR virtual cluster console, you can access logs from the Spark History UI. […]

Use Amazon Athena parameterized queries to provide data as a service

Amazon Athena now provides you more flexibility to use parameterized queries, and we recommend you use them as the best practice for your Athena queries moving forward so you benefit from the security, reusability, and simplicity they offer. In a previous post, Improve reusability and security using Amazon Athena parameterized queries, we explained how parameterized […]

Accelerate machine learning with AWS Data Exchange and Amazon Redshift ML

July 2023: This post was reviewed for accuracy and updated. Amazon Redshift ML makes it easy for SQL users to create, train, and deploy ML models using familiar SQL commands. Redshift ML allows you to use your data in Amazon Redshift with Amazon SageMaker, a fully managed ML service, without requiring you to become an […]

Analyze logs with Dynatrace Davis AI Engine using Amazon Kinesis Data Firehose HTTP endpoint delivery

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. This blog post is co-authored with Erick Leon, Sr. Technical Alliance Manager from Dynatrace. Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into data lakes, data stores, and […]

How William Hill migrated NoSQL workloads at scale to Amazon Keyspaces

Social gaming and online sports betting are competitive environments. The game must be able to handle large volumes of unpredictable traffic while simultaneously promising zero downtime. In this domain, user retention is no longer just desirable, it’s critical. William Hill is a global online gambling company based in London, England, and it is the founding […]

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Sink Amazon Kinesis Data Analytics Apache Flink output to Amazon Keyspaces using Apache Cassandra Connector

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed Apache Cassandra–compatible database service. With Amazon Keyspaces you don’t have to provision, patch, or manage […]

Build an Apache Iceberg data lake using Amazon Athena, Amazon EMR, and AWS Glue

March 2024: This post was reviewed and updated for accuracy. Most businesses store their critical data in a data lake, where you can bring data from various sources to a centralized storage. The data is processed by specialized big data compute engines, such as Amazon Athena for interactive queries, Amazon EMR for Apache Spark applications, […]