Remove 2015 Remove Data Analytics Remove Data Lake Remove Optimization
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Speed up queries with the cost-based optimizer in Amazon Athena

AWS Big Data

You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) data lake and 30 data sources, including on-premises data sources or other cloud systems using SQL or Python. Let’s discuss some of the cost-based optimization techniques that contributed to improved query performance.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. The timestamp clause lets us travel back without altering current data.

Snapshot 100
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Run Spark SQL on Amazon Athena Spark

AWS Big Data

Modern applications store massive amounts of data on Amazon Simple Storage Service (Amazon S3) data lakes, providing cost-effective and highly durable storage, and allowing you to run analytics and machine learning (ML) from your data lake to generate insights on your data.

Data Lake 101
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How SumUp made digital analytics more accessible using AWS Glue

AWS Big Data

In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

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Extend your data mesh with Amazon Athena and federated views

AWS Big Data

Recently, Athena added support for creating and querying views on federated data sources to bring greater flexibility and ease of use to use cases such as interactive analysis and business intelligence reporting. Big Data Architect on Amazon Athena. Let’s dive into the solution. Pathik Shah is a Sr.

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Three Trends for Modernizing Analytics and Data Warehousing in 2019

Cloudera

Data analytics priorities have shifted this year. Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments. Natural language analytics and streaming data analytics are emerging technologies that will impact the market.

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Turning Streams Into Data Products

Cloudera

Organizations are increasingly building low-latency, data-driven applications, automations, and intelligence from real-time data streams. Cloudera Stream Processing (CSP) enables customers to turn streams into data products by providing capabilities to analyze streaming data for complex patterns and gain actionable intel.