Data Integration at Speed and Scale
With the proliferation of data sources and user data, many organizations are overwhelmed with the time required to prepare and serve data for various use cases. In today's business world, speed is essential, and digitally savvy enterprises are finding success by increasing collaboration and pushing out data-driven insights in a format that everyone can consume and act on quickly.
At Trianz, we aim to help organizations find a highly scalable and cost-effective solution to transforming their legacy systems with AWS Glue. As an AWS Advanced Partner, our AWS Glue capabilities include:
Building and deploying secure data pipelines using AWS Glue best practices
Setup and implementation of Glue Data Catalog, Lake Formation, DataOps, and data security using Key Management Services (KMS)
Migrating legacy ETL workloads to AWS Glue
Operationalizing and managing enterprise PaaS environments and their data portfolios in a governed manner
No matter the use case, our expert teams are here to empower your organization with the collaborative culture it needs to overcome today’s toughest data, persona, and application barriers.
What is AWS Glue?
What started as an ETL service in 2017— and evolved into a data preparation tool — is now a full-fledged data integration service used by hundreds of thousands of organizations worldwide. AWS Glue runs in a serverless environment, meaning there is no need for provisioning, configuring, or spinning up servers, and users only pay for the time they use.
With the pay-as-you-go model and the capability to integrate petabyte-scale volumes of data, Glue is quickly becoming a popular choice for building secure and scalable data lakes, warehouses, lakehouses, and data mesh architectures.
Benefits of AWS Glue
There is no infrastructure to maintain, and Glue automatically allocates required compute power and run jobs.
Glue’s all-in-one pricing model is 55% cheaper than other cloud data integration options.
Users have the option to develop data integration pipelines in open source using SparkSQL, PySpark, and Scala.
Development environments are catered to different skillsets: Visual ETL development for data engineers, notebook-styled development for Data scientists, and no-code development for Data Analysts.
Handles Complex Workloads
Connect to 200+ data sources and process petabytes of data using batch, streaming, events, and interactive API-based execution modes.
AWS Glue Case Studies
Enabling Machine Learning
A leading global retail chain wanted to acquire omnichannel sales and marketing analytics and customer 360 analytics to improve customer loyalty and increase digital sales. They required the migration of its legacy data and analytics platform to a modern cloud architecture on AWS.
To learn how Trianz used AWS Glue to enable machine learning use cases and help them better understand customer behavior, read this case study on Transforming Digital Marketing Operations for a Global Retail Chain.
Cloud Analytics Solution
A leading global healthcare provider wanted to leverage cloud infrastructure to build a secure, scalable, and industry-compliant cloud IT platform. They required modern and highly compliant analytics capabilities to generate healthcare insights for their US and EU geographic areas.
To learn how Trianz used AWS Glue to build a flexible, scalable, and secure analytics platform that was aligned with strict guidelines and regulations, read this case study on Building a Global Data Platform on the AWS Cloud.
AWS Glue Use Cases
Glue as an extract, transform, load (ETL) service is used to develop complex ETL workflows in a straightforward UI-based environment. It provides three visual interfaces that allow data engineers, ETL developers, and analysts to create ETL workstreams, with little to no additional coding required.
Users simply create jobs using table definitions in the AWS Glue Data Catalog, set triggers to initiate the jobs, point the crawler in the source direction to retrieve data, and Glue automatically generates the code required to transform the data from source to target. With Glue’s three-step process, users can complete an ETL job in minutes instead of months.
AWS Glue streamlines data preparation with DataBrew, a visual data preparation tool that makes it easy for data scientists to clean and normalize data. Users can choose from over 350 pre-built transformations capable of automatically transforming data into a format ready for analytics and machine learning. There is no upfront commitment, and users only pay for the time they spend using DataBrew.
AWS Glue and AWS Lake Formation are essential components for building data lakes and lakehouses. Glue provides the data catalog and crawlers for seamless data movement between AWS services. AWS Lake Formation allows for the data to be centralized, curated, and secured as a data lake or lakehouse. In short, AWS Lake Formation provides the fine-grained access controls for security and governance, and AWS Glue simplifies the metadata and data discovery for data lake analytics. What the user ends up with is a cost-effective, well-governed, and highly scalable data store solution.