Category: Analytics

This post is co-written by Parijat Sharma: Principal, Strategy & Transformation, Wenhao Xia: Manager, Data Science, Vineeth Sandadi: Manager, Business Consulting from ZS Associates, Inc, Arianna Tousi: Strategy, Insights and Planning Consultant from ZS, Gopi Vikranth: Associate Principal from ZS. In their own words, “We’re passionately committed to helping our clients and their customers thrive, […]

Onboarding new data or building new analytics pipelines in traditional analytics architectures typically requires extensive coordination across business, data engineering, and data science and analytics teams to first negotiate requirements, schema, infrastructure capacity needs, and workload management. For a large number of use cases today however, business users, data scientists, and analysts are demanding easy, […]

The AWS Marketing Data Science and Engineering team enables AWS Marketing to measure the effectiveness and impact of various marketing initiatives and campaigns. This is done through a data platform and infrastructure strategy that consists of maintaining data warehouse, data lake, and data transformation (ETL) pipelines, and designing software tools and services to run related […]

As a data warehouse administrator or data engineer, you may need to perform maintenance tasks and activities or perform some level of custom monitoring on a regular basis. You can combine these activities inside a stored procedure or invoke views to get details. Some of these activities include things like loading nightly staging tables, invoking […]

Amazon EMR now supports the capacity-optimized allocation strategy for Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances for launching Spot Instances from the most available Spot Instance capacity pools by analyzing capacity metrics in real time. You can now specify up to 15 instance types in your EMR task instance fleet configuration. This provides Amazon […]

Data lakes give organizations the ability to harness data from multiple sources in less time. Users across different roles are now empowered to collaborate and analyze data in different ways, leading to better, faster decision-making. Amazon Simple Storage Service (Amazon S3) is the highly performant object storage service for structured and unstructured data and the […]

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL, business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per day and power analytics […]

Healthcare organizations across the globe strive to provide the best possible patient care at the lowest cost. In a patient’s care journey, multiple organizations are often involved, including the healthcare provider, diagnostic labs, pharmacies, and health insurance payors. Each of these organizations needs to exchange health data efficiently with the others to ensure care continuity […]

Organizations across verticals have been building streaming-based extract, transform, and load (ETL) applications to more efficiently extract meaningful insights from their datasets. Although streaming ingest and stream processing frameworks have evolved over the past few years, there is now a surge in demand for building streaming pipelines that are completely serverless. Since 2017, AWS Glue […]

Amazon EMR allows you to process vast amounts of data quickly and cost-effectively at scale. Using open-source tools such as Apache Spark, Apache Hive, and Presto, and coupled with the scalable storage of Amazon Simple Storage Service (Amazon S3), Amazon EMR gives analytical teams the engines and elasticity to run petabyte-scale analysis for a fraction […]