AWS Big Data Blog

Category: AWS Glue

Simplify data integration pipeline development using AWS Glue custom blueprints

Organizations spend significant time developing and maintaining data integration pipelines that hydrate data warehouses, data lakes, and lake houses. As data volume increases, data engineering teams struggle to keep up with new requests from business teams. Although these requests may come from different teams, they’re often similar, such as ingesting raw data from a source […]

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Simplify Snowflake data loading and processing with AWS Glue DataBrew

Historically, inserting and retrieving data from a given database platform has been easier compared to a multi-platform architecture for the same operations. To simplify bringing data in from a multi-database platform, AWS Glue DataBrew supports bringing your data in from multiple data sources via the AWS Glue Data Catalog. However, this requires you to have […]

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Doing data preparation using on-premises PostgreSQL databases with AWS Glue DataBrew

Today, with AWS Glue DataBrew, data analysts and data scientists can easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (Amazon S3) data lake, Amazon Redshift data warehouse, and Amazon Aurora and Amazon Relational Database Service (Amazon RDS) databases. Customers can choose from over 250 […]

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Migrate terabytes of data quickly from Google Cloud to Amazon S3 with AWS Glue Connector for Google BigQuery

The cloud is often seen as advantageous for data lakes because of better security, faster time to deployment, better availability, more frequent feature and functionality updates, more elasticity, more geographic coverage, and costs linked to actual utilization. However, recent studies from Gartner and Harvard Business Review show multi-cloud and intercloud architectures are something leaders need […]

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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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How 1Strategy simplified their spreadsheet ETL process using AWS Glue DataBrew

This is a guest blog post by Pat Reilly and Gary Houk at 1Strategy. In their own words, “1Strategy is an APN Premier Consulting Partner focusing exclusively on AWS solutions. 1Strategy consultants help businesses architect, migrate, and optimize their workloads on AWS, creating scalable, cost-effective, secure, and reliable solutions. 1Strategy holds the AWS DevOps, Migration, […]

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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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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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