AWS Big Data Blog

Category: Serverless

Let’s look at PyDeequ’s main components, and how they relate to Deequ (shown in the following diagram)

Testing data quality at scale with PyDeequ

June 2024: This post was reviewed and updated to add instructions for using PyDeequ with HAQM SageMaker Notebook, SageMaker Studio, EMR, and updated the examples against a new dataset. March 2023: You can now use AWS Glue Data Quality to measure and manage the quality of your data. AWS Glue Data Quality is built on Deequ […]

As illustrated in the following architecture diagram, the DQAF exclusively uses serverless AWS technology.

Building a serverless data quality and analysis framework with Deequ and AWS Glue

March 2023: You can now use AWS Glue Data Quality to measure and manage the quality of your data. AWS Glue Data Quality is built on DeeQu and it offers a simplified user experience for customers who want to this open-source package. Refer to the blog and documentation for additional details. With ever-increasing amounts of data […]

This blog covers use case based walkthroughs of how we can achieve the top 7 among those transformations in AWS Glue DataBrew.

7 most common data preparation transformations in AWS Glue DataBrew

For all analytics and ML modeling use cases, data analysts and data scientists spend a bulk of their time running data preparation tasks manually to get a clean and formatted data to meet their needs. We ran a survey among data scientists and data analysts to understand the most frequently used transformations in their data […]

The following diagram shows the workflow to connect Apache Airflow to HAQM EMR.

Dream11’s journey to building their Data Highway on AWS

This is a guest post co-authored by Pradip Thoke of Dream11. In their own words, “Dream11, the flagship brand of Dream Sports, is India’s biggest fantasy sports platform, with more than 100 million users. We have infused the latest technologies of analytics, machine learning, social networks, and media technologies to enhance our users’ experience. Dream11 […]

We’ll walk through a solution that takes sets up a recurring Profile job to determine data quality metrics, and using your defined business rules.

Setting up automated data quality workflows and alerts using AWS Glue DataBrew and AWS Lambda

Proper data management is critical to successful, data-driven decision-making. An increasingly large number of customers are adopting data lakes to realize deeper insights from big data. As part of this, you need clean and trusted data in order to gain insights that lead to improvements in your business. As the saying goes, garbage in is […]

Ingesting Jira data into HAQM S3

Consolidating data from a work management tool like Jira and integrating this data with other data sources like ServiceNow, GitHub, Jenkins, and Time Entry Systems enables end-to-end visibility of different aspects of the software development lifecycle and helps keep your projects on schedule and within budget. HAQM Simple Storage Service (HAQM S3) is an object […]

Transform data and create dashboards simply using AWS Glue DataBrew and HAQM QuickSight

Before you can create visuals and dashboards that convey useful information, you need to transform and prepare the underlying data. The range and complexity of data transformation steps required depends on the visuals you would like in your dashboard. Often, the data transformation process is time-consuming and highly iterative, especially when you are working with […]

Building an ad-to-order conversion engine with HAQM Kinesis, AWS Glue, and HAQM QuickSight

August 30, 2023: HAQM Kinesis Data Analytics has been renamed to HAQM Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Businesses in ecommerce have the challenge of measuring their ad-to-order conversion ratio for ads or promotional campaigns displayed on a webpage. Tracking the number of users that […]

Preparing data for ML models using AWS Glue DataBrew in a Jupyter notebook

AWS Glue DataBrew is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning (ML). In this post, we examine a sample ML use case and show how to use DataBrew and a Jupyter notebook to […]

Enabling self-service data publication to your data lake using AWS Glue DataBrew

Data lakes have been providing a level of flexibility to organizations unparalleled to anything before them. Having the ability to load and query data in place—and in its natural form—has led to an explosion of data lake deployments that have allowed organizations to accelerate against their data strategy faster than ever before. Most organizations have […]