Category: Storage

This post is co-written with Dr. Quan Hoang Nguyen, CTO at Fantom Foundation. Here at Fantom Foundation (Fantom), we have developed a high performance, highly scalable, and secure smart contract platform. It’s designed to overcome limitations of the previous generation of blockchain platforms. The Fantom platform is permissionless, decentralized, and open source. The majority of […]

AWS Glue includes crawlers, a capability that make discovering datasets simpler by scanning data in Amazon Simple Storage Service (Amazon S3) and relational databases, extracting their schema, and automatically populating the AWS Glue Data Catalog, which keeps the metadata current. This reduces the time to insight by making newly ingested data quickly available for analysis […]

Traditionally, Spark workloads have been run on a dedicated setup like a Hadoop stack with YARN or MESOS as a resource manager. Starting from Apache Spark 2.3, Spark added support for Kubernetes as a resource manager. The new Kubernetes scheduler natively supports the submission of Spark jobs to a Kubernetes cluster. Spark on Kubernetes provides […]

In legacy relational database management systems, data is stored in several complex data types, such XML, JSON, BLOB, or CLOB. This data might contain valuable information that is often difficult to transform into insights, so you might be looking for ways to load and use this data in a modern cloud data warehouse such as […]

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In 2019, Athena added support for federated queries to run SQL […]

With real-time information about customers, products, and applications in hand, organizations can take action as events happen in their business application. For example, you can prevent financial fraud, deliver personalized offers, and identify and prevent failures before they occur in near real time. Although batch analytics provides abilities to analyze trends and process data at […]

Many organizations prefer SQL for data preparation because they already have developers for extract, transform, and load (ETL) jobs and analysts preparing data for machine learning (ML) who understand and write SQL queries. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using […]

Data lakes use a ring of purpose-built data services around a central data lake. Data needs to move between these services and data stores easily and securely. The following are some examples of such services: Amazon Simple Storage Service (Amazon S3), which stores structured, unstructured, and semi-structured data Amazon Redshift, a fully managed, petabyte-scale data […]

Customers are increasingly building data lakes to store data at massive scale in the cloud. It’s common to use distributed computing engines, cloud-native databases, and data warehouses when you want to process and analyze your data in data lakes. Amazon EMR and AWS Glue are two key services you can use for such use cases. […]

The AWS Well-Architected Data Analytics Lens provides a set of guiding principles for analytics applications on AWS. One of the best practices it talks about is build a central Data Catalog to store, share, and track metadata changes. AWS Glue provides a Data Catalog to fulfill this requirement. AWS Glue also provides crawlers that automatically […]