Updated 2/14/2020: Updates have been made to support the latest versions of EMR and Apache Ranger 2.0. Support for Ranger Presto Plugin has been added which requires Apache Ranger 2.0. NOTE: Ranger Presto Plugin has been tested only with the EMR 5.29. Updated 9/26/2018: Updates have been made to support Read more…
The Amazon Redshift materialized views function helps you achieve significantly faster query performance on repeated or predictable workloads such as dashboard queries from Business Intelligence (BI) tools, such as Amazon QuickSight. It also speeds up and simplifies extract, load, and transform (ELT) data processing. You can use materialized views to Read more…
Amazon Redshift Federated Query allows you to combine the data from one or more Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL databases with data already in Amazon Redshift. You can also combine such data with data in an Amazon S3 data lake. This post shows you how to set Read more…
You may have heard the saying that the best ETL is no ETL. Amazon Redshift now makes this possible with Federated Query. In its initial release, this feature lets you query data in Amazon Aurora PostgreSQL or Amazon RDS for PostgreSQL using Amazon Redshift external schemas. Federated Query also exposes Read more…
This post demonstrates a serverless, cloud-based approach to building a network performance analytics solution using AWS services that can provide flexibility and performance while keeping costs under control with pay-per-use AWS services. Without good network performance, you may struggle to face the challenges of real-time and low latency services and Read more…
To grow and develop your business, you must collect data from a myriad of sources (such as relational and NoSQL databases, clickstream, and IoT events) and combine it using analytics to extract insight. AWS Lake Formation enables you to ingest data from many different sources into a data lake based Read more…
Apache Spark is a powerful data processing engine that gives data analyst and engineering teams easy to use APIs and tools to analyze their data, but it can be challenging for teams to manage their Python and R library dependencies. Installing every dependency that a job may need before it Read more…
Customers use Apache Hive with Amazon EMR to provide SQL-based access to petabytes of data stored on Amazon S3. Amazon EMR 6.0.0 adds support for Hive LLAP, providing an average performance speedup of 2x over EMR 5.29, with up to 10x improvement on individual Hive TPC-DS queries. This post shows Read more…
Customers use Apache Hive with Amazon EMR to provide SQL-based access to petabytes of data stored on Amazon S3. Amazon EMR 6.0.0 adds support for Hive LLAP, providing an average performance speedup of 2x over EMR 5.29, with up to 10x improvement on individual Hive TPC-DS queries. This post shows Read more…
Customers use Apache Hive with Amazon EMR to provide SQL-based access to petabytes of data stored on Amazon S3. Amazon EMR 6.0.0 adds support for Hive LLAP, providing an average performance speedup of 2x over EMR 5.29, with up to 10x improvement on individual Hive TPC-DS queries. This post shows Read more…