Category: Analytics

Enterprises often want to centralize governance and compliance requirements, and provide a common set of policies on how Amazon EMR instances should be set up. You can use AWS Service Catalog to centrally manage commonly deployed Amazon EMR cluster configurations, and this helps you achieve consistent governance and meet your compliance requirements, while at the […]

Customers use Amazon Redshift for everything from accelerating existing database environments, to ingesting weblogs for big data analytics. Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that offers simple operations and high performance. Amazon Redshift provides an open standard JDBC/ODBC driver interface, which allows you to connect your existing business intelligence (BI) tools and reuse existing analytics queries. Amazon Redshift can run any type of data model, from a production transaction system third-normal-form model to star and snowflake schemas, data vault, or simple flat tables. This post takes you through the most common performance-related opportunities when adopting Amazon Redshift and gives you concrete guidance on how to optimize each one. 

This post provides guidance on how to configure Amazon Athena federation with AWS Lambda and Amazon Redshift, while addressing performance considerations to ensure proper use.

Amazon QuickSight is an analytics service that you can use to create datasets, perform one-time analyses, and build visualizations and dashboards. In an enterprise deployment of QuickSight, you can have multiple dashboards, and each dashboard can have multiple visualizations based on multiple datasets. This can quickly become a management overhead to view all the datasets’ […]

This is a guest post by Pradnya Bhandary, Co-Founder and CEO at Bryte Systems. Data can be transformative for an organization. How and where you store your data for analysis and business intelligence is therefore an especially important decision that each organization needs to make. Should you choose an on-premises data warehouse solution or embrace […]

When we look at enterprise data warehousing systems, we receive data in various formats, such as XML, JSON, or CSV. Most third-party system integrations happen through SOAP or REST web services, where the input and output data format is either XML or JSON. When applications deal with CSV or JSON, it becomes fairly simple to […]

One of our favorite things about working on Amazon Redshift, the cloud data warehouse service at AWS, is the inspiring stories from customers about how they’re using data to gain business insights. Many of our recent engagements have been with customers upgrading to the new instance type, Amazon Redshift RA3 with managed storage. In this […]

Toyota Motor Corporation (TMC), a global automotive manufacturer, has made “connected cars” a core priority as part of its broader transformation from an auto company to a mobility company. In recent years, TMC and its affiliate technology and big data company, Toyota Connected, have developed an array of new technologies to provide connected services that […]

Developing extract, transform, and load (ETL) data pipelines is one of the most time-consuming steps to keep data lakes, data warehouses, and databases up to date and ready to provide business insights. You can categorize these pipelines into distributed and non-distributed, and the choice of one or the other depends on the amount of data […]

With the release of Kinesis Data Firehose HTTP endpoint delivery, you can now stream your data through Amazon Kinesis or directly push data to Kinesis Data Firehose and configure it to deliver data to MongoDB Atlas. You can also configure Kinesis Data Firehose to transform the data before delivering it to its destination. You don’t have to write applications and manage resources to read data and push to MongoDB. It’s all managed by AWS, making it easier to estimate costs for your data based on your data volume. In this post, we discuss how to integrate Kinesis Data Firehose and MongoDB Cloud and demonstrate how to stream data from your source to MongoDB Atlas.