Category: Database

In today’s digital world, data is generated by a large number of disparate sources and growing at an exponential rate. Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive […]

DynamoDB zero-ETL integration with Amazon Redshift is now generally available and provides fully-managed replication of DynamoDB tables into an Amazon Redshift database. Learn more at DynamoDB zero-ETL integration with Amazon Redshift. Amazon DynamoDB is a fully managed NoSQL service that delivers single-digit millisecond performance at any scale. It’s used by thousands of customers for mission-critical […]

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 toward centralizing their identity and access strategy across all their applications, including on-premises and third-party. Many organizations use Keycloak as their identity provider […]

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Recently, data lakes have gained lot of traction to become the foundation for analytical solutions, because they come with benefits such as scalability, fault tolerance, and support for structured, semi-structured, and unstructured datasets. Data lakes are not transactional by default; however, there […]

This is a guest post co-written with Jonatan Selsing and Moses Arthur from Novo Nordisk. This is the second post of a three-part series detailing how Novo Nordisk, a large pharmaceutical enterprise, partnered with AWS Professional Services to build a scalable and secure data and analytics platform. The first post of this series describes the […]

This is a guest blog post co-written with Corey Johnson from Huron. Having an accurate and up-to-date inventory of all technical assets helps an organization ensure it can keep track of all its resources with metadata information such as their assigned owners, last updated date, used by whom, how frequently, and more. It helps engineers, […]

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 […]