AWS Big Data Blog

Amazon EMR is a cloud big data platform for running large-scale distributed data processing jobs, interactive SQL queries, and machine learning (ML) applications using open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto. Apache Iceberg is an open table format for huge analytic datasets. Table formats typically indicate the format and location of […]

Amazon QuickSight is a scalable, serverless, embeddable, machine learning (ML)-powered business intelligence (BI) service built for the cloud that supports identity federation in both Standard and Enterprise editions. Organizations are working towards centralizing their identity and access strategy across all their applications, including on premises, third-party, and applications on AWS. Many organizations use identity providers […]

Modern data architectures encourage the integration of data lakes, data warehouses, and purpose-built data stores, enabling unified governance and easy data movement. With a modern data architecture on AWS, you can store data in a data lake and use a ring of purpose-built data services around the lake, allowing you to make decisions with speed […]

In this post, we discuss how to use AWS Database Migration Service (AWS DMS) native change data capture (CDC) capabilities to stream changes into Amazon Kinesis Data Streams. AWS DMS is a cloud service that makes it easy to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use […]

Amazon Managed Grafana recently announced a new data source plugin for Amazon Redshift, enabling you to query, visualize, and alert on your Amazon Redshift data from Amazon Managed Grafana workspaces. With the new Amazon Redshift data source, you can now create dashboards and alerts in your Amazon Managed Grafana workspaces to analyze your structured and […]

Nowadays, many customers have built their data lakes as the core of their data analytic systems. In a typical use case of data lakes, many concurrent queries run to retrieve consistent snapshots of business insights by aggregating query results. A large volume of data constantly comes from different data sources into the data lakes. There […]

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, Amazon SageMaker for machine learning, and Amazon QuickSight for data […]

Amazon Redshift is a popular cloud data warehouse that allows you to process exabytes of data across your data warehouse, operational database, and data lake using standard SQL. Amazon Redshift offers different node types like DC2 (dense compute) and RA3, which you can use for your different workloads and use cases. For more information about […]

As the implementation of data lakes and modern data architecture increases, customers’ expectations around its features also increase, which include ACID transaction, UPSERT, time travel, schema evolution, auto compaction, and many more. By default, Amazon Simple Storage Service (Amazon S3) objects are immutable, which means you can’t update records in your data lake because it […]

This is post is co-authored by Jyothin Madari, Madhusudhan Muppagowni and Ayush Srivastava from GE. GE Proficy Manufacturing Data Cloud (MDC), part of the GE Digital’s Manufacturing Execution Systems (MES) suite of solutions, allows GED’s customers to increase the derived value easily and quickly from the MES by reliably bringing enterprise-wide manufacturing data into the […]