AWS Big Data Blog

Category: AWS Step Functions

Develop an Amazon Redshift ETL serverless framework using RSQL, AWS Batch, and AWS Step Functions

Amazon Redshift RSQL is a command-line client for interacting with Amazon Redshift clusters and databases. You can connect to an Amazon Redshift cluster, describe database objects, query data, and view query results in various output formats. You can use enhanced control flow commands to replace existing extract, transform, load (ETL) and automation scripts. This post […]

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Trigger an AWS Glue DataBrew job based on an event generated from another DataBrew job

Organizations today have continuous incoming data, and analyzing this data in a timely fashion is becoming a common requirement for data analytics and machine learning (ML) use cases. As part of this, you need clean data in order to gain insights that can enable enterprises to get the most out of their data for business […]

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Solution Architecture

Orchestrate big data jobs on on-premises clusters with AWS Step Functions

Customers with specific needs to run big data compute jobs on an on-premises infrastructure often require a scalable orchestration solution. For large-scale distributed compute clusters, the orchestration of jobs must be scalable to maximize their utilization, while at the same time remain resilient to any failures to prevent blocking the ever-growing influx of data and […]

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Simplify your ETL and ML pipelines using the Amazon Athena UNLOAD feature

Many organizations prefer SQL for data preparation because they already have developers for extract, transform, and load (ETL) jobs and analysts preparing data for machine learning (ML) who understand and write SQL queries. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using […]

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GDAC architecture

How the Georgia Data Analytics Center built a cloud analytics solution from scratch with the AWS Data Lab

This is a guest post by Kanti Chalasani, Division Director at Georgia Data Analytics Center (GDAC). GDAC is housed within the Georgia Office of Planning and Budget to facilitate governed data sharing between various state agencies and departments. The Office of Planning and Budget (OPB) established the Georgia Data Analytics Center (GDAC) with the intent […]

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ETL orchestration using the Amazon Redshift Data API and AWS Step Functions with AWS SDK integration

Extract, transform, and load (ETL) serverless orchestration architecture applications are becoming popular with many customers. These applications offers greater extensibility and simplicity, making it easier to maintain and simplify ETL pipelines. A primary benefit of this architecture is that we simplify an existing ETL pipeline with AWS Step Functions and directly call the Amazon Redshift […]

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Featured Stateful Architecture

Doing more with less: Moving from transactional to stateful batch processing

Amazon processes hundreds of millions of financial transactions each day, including accounts receivable, accounts payable, royalties, amortizations, and remittances, from over a hundred different business entities. All of this data is sent to the eCommerce Financial Integration (eCFI) systems, where they are recorded in the subledger. Ensuring complete financial reconciliation at this scale is critical […]

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Build and orchestrate ETL pipelines using Amazon Athena and AWS Step Functions

Extract, transform, and load (ETL) is the process of reading source data, applying transformation rules to this data, and loading it into the target structures. ETL is performed for various reasons. Sometimes ETL helps align source data to target data structures, whereas other times ETL is done to derive business value by cleansing, standardizing, combining, […]

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Prepare, transform, and orchestrate your data using AWS Glue DataBrew, AWS Glue ETL, and AWS Step Functions

Data volumes in organizations are increasing at an unprecedented rate, exploding from terabytes to petabytes and in some cases exabytes. As data volume increases, it attracts more and more users and applications to use the data in many different ways—sometime referred to as data gravity. As data gravity increases, we need to find tools and […]

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Scope of Solution

Centralize feature engineering with AWS Step Functions and AWS Glue DataBrew

One of the key phases of a machine learning (ML) workflow is data preprocessing, which involves cleaning, exploring, and transforming the data. AWS Glue DataBrew, announced in AWS re:Invent 2020, is a visual data preparation tool that enables you to develop common data preparation steps without having to write any code or installation. In this […]

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