AWS Big Data Blog
Category: Learning Levels
Empower your Jira data in a data lake with Amazon AppFlow and AWS Glue
In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]
Migrate your existing SQL-based ETL workload to an AWS serverless ETL infrastructure using AWS Glue
Data has become an integral part of most companies, and the complexity of data processing is increasing rapidly with the exponential growth in the amount and variety of data. Data engineering teams are faced with the following challenges: Manipulating data to make it consumable by business users Building and improving extract, transform, and load (ETL) […]
A side-by-side comparison of Apache Spark and Apache Flink for common streaming use cases
Apache Flink and Apache Spark are both open-source, distributed data processing frameworks used widely for big data processing and analytics. Spark is known for its ease of use, high-level APIs, and the ability to process large amounts of data. Flink shines in its ability to handle processing of data streams in real-time and low-latency stateful […]
Extend your data mesh with Amazon Athena and federated views
Amazon Athena is a serverless, interactive analytics service built on the Trino, PrestoDB, and Apache Spark open-source frameworks. You can use Athena to run SQL queries on petabytes of data stored on Amazon Simple Storage Service (Amazon S3) in widely used formats such as Parquet and open-table formats like Apache Iceberg, Apache Hudi, and Delta […]
Simplify external object access in Amazon Redshift using automatic mounting of the AWS Glue Data Catalog
Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service delivering the best price-performance. Today, tens of thousands of customers run business-critical workloads on Amazon Redshift to cost-effectively and quickly analyze their data using standard SQL and existing business intelligence (BI) tools. Amazon Redshift now makes it easier for you to run queries in AWS […]
Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift
The GDPR (General Data Protection Regulation) right to be forgotten, also known as the right to erasure, gives individuals the right to request the deletion of their personally identifiable information (PII) data held by organizations. This means that individuals can ask companies to erase their personal data from their systems and any third parties with […]
Find the best Amazon Redshift configuration for your workload using Redshift Test Drive
Amazon Redshift is a widely used, fully managed, petabyte-scale cloud data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. With the launch of Amazon Redshift Serverless and the various deployment options Amazon Redshift provides (such as instance types and cluster sizes), customers […]
Near-real-time analytics using Amazon Redshift streaming ingestion with Amazon Kinesis Data Streams and Amazon DynamoDB
Amazon Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, easy, and secure analytics at scale. Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the widely used cloud data warehouse. You can run and […]
Improved scalability and resiliency for Amazon EMR on EC2 clusters
Amazon EMR is the cloud big data solution for petabyte-scale data processing, interactive analytics, and machine learning using open-source frameworks such as Apache Spark, Apache Hive, and Presto. Customers asked us for features that would further improve the resiliency and scalability of their Amazon EMR on EC2 clusters, including their large, long-running clusters. We have […]
End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue
Data is a key enabler for your business. Many AWS customers have integrated their data across multiple data sources using AWS Glue, a serverless data integration service, in order to make data-driven business decisions. To grow the power of data at scale for the long term, it’s highly recommended to design an end-to-end development lifecycle […]