AWS Big Data Blog

Category: AWS Big Data

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Analyzing petabytes of trade and quote data with Amazon FinSpace

We recently announced Amazon FinSpace, a fully-managed data management and analytics service that makes it easy to store, catalog, and prepare financial industry data at scale, reducing the time it takes for financial services industry (FSI) customers to find and access all types of financial data for analysis from months to minutes. Financial services organizations […]

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The following graph shows performance improvements measured as total runtime for TPC-DS queries. Amazon EMR 5.31 with EMR runtime has the better (lower) runtime.

Amazon EMR introduces EMR runtime for Presto, providing a 2.6 times speedup

Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics, and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook. Running Presto […]

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Extract multidimensional data from Microsoft SQL Server Analysis Services using AWS Glue

AWS Glue is fully managed service that makes it easier for you to extract, transform, and load (ETL) data for analytics. You can easily create ETL jobs to connect to backend data sources. There are several natively supported data sources, but what if you need to extract data from an unsupported data source? What if […]

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Effective data lakes using AWS Lake Formation, Part 2: Creating a governed table for streaming data sources

We announced the preview of AWS Lake Formation transactions, row-level security, and acceleration at AWS re:Invent 2020. In Part 1 of this series, we explained how to set up a governed table and add objects to it. In this post, we expand on this example, and demonstrate how to ingest streaming data into governed tables using Lake Formation transactions. […]

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The following graph shows that the minimum throughput achieved with the persistent HFile

Amazon EMR 6.2.0 adds persistent HFile tracking to improve performance with HBase on Amazon S3

Apache HBase is an open-source, NoSQL database that you can use to achieve low latency random access to billions of rows. Starting with Amazon EMR 5.2.0, you can enable HBase on Amazon Simple Storage Service (Amazon S3). With HBase on Amazon S3, the HBase data files (HFiles) are written to Amazon S3, enabling data lake […]

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Automate dynamic mapping and renaming of column names in data files using AWS Glue: Part 2

In Part 1 of this two-part post, we looked at how we can create an AWS Glue ETL job that is agnostic enough to rename columns of a data file by mapping to column names of another file. The solution focused on using a single file that was populated in the AWS Glue Data Catalog […]

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Automate dynamic mapping and renaming of column names in data files using AWS Glue: Part 1

A common challenge ETL and big data developers face is working with data files that don’t have proper name header records. They’re tasked with renaming the columns of the data files appropriately so that downstream application and mappings for data load can work seamlessly. One example use case is while working with ORC files and […]

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Top 9 performance tuning tips for PrestoDB on Amazon EMR

Presto is a popular distributed SQL query engine for interactive data analytics. With its massively parallel processing (MPP) architecture, it’s capable of directly querying large datasets without the need of time-consuming and costly ETL processes. With a properly tuned Presto cluster you can run fast queries against big data with response times ranging from subsecond […]

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Ingest Salesforce data into Amazon S3 using the CData JDBC custom connector with AWS Glue

Organizations that successfully generate business value from their data will outperform their peers. Many AWS customers require a data storage and analytics solution that combines the prospect information stored in Salesforce, a popular and widely used customer relationship management (CRM) platform, with other structured and unstructured data in their data lake to innovate and build […]

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Effective data lakes using AWS Lake Formation, Part 1: Getting started with governed tables

Thousands of customers are building their data lakes on Amazon Simple Storage Service (Amazon S3). You can use AWS Lake Formation to build your data lakes easily—in a matter of days as opposed to months. However, there are still some difficult challenges to address with your data lakes: Supporting streaming updates and deletes in your data […]

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