Category: HAQM SageMaker

July 2023: This post was reviewed for accuracy. Real-time insight is critical when it comes to building trading strategies. Any delay in data insight can cost lot of money to the traders. Often, you need to look at historical market trends to predict future trading pattern and make the right bid. More the historical data […]

Ensuring data reliability is one of the key objectives of maintaining data integrity and is crucial for building data trust across an organization. Data reliability means that the data is complete and accurate. It’s the catalyst for delivering trusted data analytics and insights. Incomplete or inaccurate data leads business leaders and data analysts to make […]

This post is co-written with data engineers, Anton Morozov and James Phillips, from Weatherbug. WeatherBug is a brand owned by GroundTruth, based in New York City, that provides location-based advertising solutions to businesses. WeatherBug consists of a mobile app reporting live and forecast data on hyperlocal weather to consumer users. The WeatherBug Data Engineering team […]

February 9, 2024: HAQM Kinesis Data Firehose has been renamed to HAQM Data Firehose. Read the AWS What’s New post to learn more. MEDHOST has been providing products and services to healthcare facilities of all types and sizes for over 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their […]

This is a guest post by Ori Nakar, Principal Engineer at Imperva. In their own words, “Imperva is a large cyber security company and an AWS Partner Network (APN) Advanced Technology Partner, who protects web applications and data assets. Imperva protects over 6,200 enterprises worldwide and many of them use Imperva Web Application Firewall (WAF) […]

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 […]

April 2024: This post was reviewed for accuracy. Developing extract, transform, and load (ETL) data pipelines is one of the most time-consuming steps to keep data lakes, data warehouses, and databases up to date and ready to provide business insights. You can categorize these pipelines into distributed and non-distributed, and the choice of one or […]

This post walks you through accessing the AWS COVID-19 data lake through the AWS Glue Data Catalog via HAQM SageMaker or Jupyter and using the open-source AWS Data Wrangler library. AWS Data Wrangler is an open-source Python package that extends the power of Pandas library to AWS and connects DataFrames and AWS data-related services (such as HAQM Redshift, HAQM S3, AWS Glue, HAQM Athena, and HAQM EMR). For more information about what you can build by using this data lake, see the associated public Jupyter notebook on GitHub.

Imagine you can see the future—to know how many customers will order your product months ahead of time so you can make adequate provisions, or to know how many of your employees will leave your organization several months in advance so you can take preemptive actions to encourage staff retention. For an organization that sees […]

This post outlines the approach taken by Intuit, though it is important to remember that there are many ways to build a data lake (for example, AWS Lake Formation). We’ll cover the technologies and processes involved in creating the Intuit Data Lake at a high level, including the overall structure and the automation used in provisioning accounts and resources. Watch this space in the future for more detailed blog posts on specific aspects of the system, from the other teams and engineers who worked together to build the Intuit Data Lake.