AWS Big Data Blog

Category: Amazon SageMaker

How BookMyShow saved 80% in costs by migrating to an AWS modern data architecture

This is a guest post co-authored by Mahesh Vandi Chalil, Chief Technology Officer of BookMyShow. BookMyShow (BMS), a leading entertainment company in India, provides an online ticketing platform for movies, plays, concerts, and sporting events. Selling up to 200 million tickets on an annual run rate basis (pre-COVID) to customers in India, Sri Lanka, Singapore, […]

Create, Train and Deploy Multi Layer Perceptron (MLP) models using Amazon Redshift ML

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse which is being used by tens of thousands of customers to process exabytes of data every day to power their analytics workloads. Amazon Redshift comes with a feature called Amazon Redshift ML which puts the power of machine learning in the hands of every […]

Use a linear learner algorithm in Amazon Redshift ML to solve regression and classification problems

Amazon Redshift is a fast, petabyte-scale cloud data warehouse delivering the best price–performance. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Amazon Redshift ML, powered by Amazon SageMaker, makes it easy for SQL users such as data analysts, data scientists, and database developers […]

Secure data movement across Amazon S3 and Amazon Redshift using role chaining and ASSUMEROLE

Data lakes use a ring of purpose-built data services around a central data lake. Data needs to move between these services and data stores easily and securely. The following are some examples of such services: Amazon Simple Storage Service (Amazon S3), which stores structured, unstructured, and semi-structured data Amazon Redshift, a fully managed, petabyte-scale data […]

Backtest trading strategies with Amazon Kinesis Data Streams long-term retention and Amazon SageMaker

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 you analyze, better trading prediction you get. Back […]

Provide data reliability in Amazon Redshift at scale using Great Expectations library

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 […]

WeatherBug reduced ETL latency to 30 times faster using Amazon Redshift Spectrum

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 […]

How MEDHOST’s cardiac risk prediction successfully leveraged AWS analytic services

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 patient care and operational excellence with its integrated clinical and financial EHR solutions. MEDHOST also offers a comprehensive Emergency Department Information System with […]

How Imperva uses Amazon Athena for machine learning botnets detection

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) […]

Effective data lakes using AWS Lake Formation, Part 3: Using ACID transactions on governed tables

Data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for all enterprise data and serve as common choice for a large number of users querying from a variety of analytics and ML tools. Often times you want to ingest data continuously into the data lake from multiple sources and query against the […]