Category: Database

Customers use Amazon Redshift to run their business-critical analytics on petabytes of structured and semi-structured data. Apache Spark is a popular framework that you can use to build applications for use cases such as ETL (extract, transform, and load), interactive analytics, and machine learning (ML). Apache Spark enables you to build applications in a variety […]

Data mesh is a new approach to data management. Companies across industries are using a data mesh to decentralize data management to improve data agility and get value from data. However, when a data producer shares data products on a data mesh self-serve web portal, it’s neither intuitive nor easy for a data consumer to […]

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. Apache Hive has performed pretty well for a long time. But with advancements in infrastructure such as cloud computing and multicore machines with large RAM, Apache Spark started to gain visibility by […]

Inventory management is a critical function for any business that deals with physical products. The primary challenge businesses face with inventory management is balancing the cost of holding inventory with the need to ensure that products are available when customers demand them. The consequences of poor inventory management can be severe. Overstocking can lead to […]

AWS Payments is part of the AWS Commerce Platform (CP) organization that owns the customer experience of paying AWS invoices. It helps AWS customers manage their payment methods and payment preferences, and helps customers make self-service payments to AWS. The Machine Learning, Data and Analytics (MLDA) team at AWS Payments enables data-driven decision-making across payments […]

This is a guest post co-written with Parag Doshi, Guru Havanur, and Simon Guindon from Tricentis. Tricentis is a global leader in continuous testing for DevOps, cloud, and enterprise applications. It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more […]

Amazon Redshift is a fast, fully-managed, petabyte scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. Using Amazon Redshift Serverless and Query Editor v2, you can load and query large datasets in just a few clicks and pay only for what you use. The decoupled compute and […]

Amazon DynamoDB is ideal for applications that need a flexible NoSQL database with low read and write latencies and the ability to scale storage and throughput up or down as needed without code changes or downtime. You can use DynamoDB for use cases including mobile apps, gaming, digital ad serving, live voting, audience interaction for live […]

Amazon Redshift is a fast, fully managed, petabyte-scale cloud data warehouse. You have the flexibility to choose from provisioned and serverless compute modes. You can start loading and querying large datasets conveniently in Amazon Redshift using Amazon Redshift Query Editor v2, a web-based SQL client application. Query Editor v2 empowers your technical and business teams […]

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. SOCAR is the leading Korean mobility company with strong competitiveness in car-sharing. SOCAR has become a comprehensive mobility platform in collaboration with Nine2One, an e-bike sharing service, […]