Category: AWS Big Data
Data mesh is a decentralized approach to data management which strives to develop the data platform from a technology-led and project-centric model into a paradigm about federated business-led and product-centric data, by design. Learn how AWS and Accenture are helping customers rapidly set up data mesh architecture on AWS leveraging the newly-announced Velocity platform. Explore how Velocity’s Data Mesh Fabric component can minimize time and effort to set up data mesh architecture on AWS.
AWS experts break down how you can build a multi-tenant data ingestion and processing engine using AWS services. We examine each component of this data pipeline and examine some of the key considerations that can influence how you approach designing a SaaS multi-tenant data ingestion process. We also explore how multi-tenant streaming data can be ingested, transformed, and stored using AWS services while ensuring there are constructs built in to the pipeline to ensure secure processing of the data.
Many organizations are adopting data lakes to handle large volumes of data, and flexible pipelines to fit the needs of consuming services and teams (machine learning, business intelligence, and analytics). In this post, we’ll explore the modern data lake and how Fivetran can help accelerate time-to-value with Amazon S3 and Apache Iceberg. Fivetran offers pre-built connectors for 300+ data sources and employs ETL to land data in the warehouse or data lake.
Gogo is a global provider of broadband connectivity products and services for business aviation. It needed a qualified engineering team to undertake a complete transition of its solutions to the cloud, build a unified data platform, and streamline the best speed of the inflight internet. Learn how N-iX developed the data platform on AWS that aggregates data from over 20 different sources using Apache Spark on Amazon EMR.
Given the breadth of use cases, data lakes need to be a complete analytical environment with a variety of analytical tools, engines, and languages supporting a variety of workloads. These include traditional analytics, business intelligence, streaming event and Internet of Things (IoT) processing, advanced machine learning, and artificial intelligence processing. Learn how Cazena builds and deploys a production ready data lake in minutes for customers.
Historically, mainframes have hosted core-business processes, applications, and data, all of which are blocked in these rigid and expensive systems. AWS and Qlik can liberate mainframe data in real-time, enabling customers to exploit its full business value for data lakes, analytics, innovation, or modernization purposes. In this post, we describe how customers use Qlik Replicate real-time data streaming to put mainframe core-business data onto AWS.
Software-as-a-Service (SaaS) presents developers and architects with a unique set of challenges. One essential decision you’ll have to make is how to partition data for each tenant of your system. Learn how to harness Amazon Redshift to build a scalable, multi-tenant SaaS solution on AWS. This post explores trategies that are commonly used to partition and isolate tenant data in a SaaS environment, and how to apply them in Amazon Redshift.
Running Hadoop, Spark, and related technologies in the cloud provides the flexibility required by these distributed systems. Cazena provides a production-ready, continuously optimized and secured Data Lake as a Service with multiple features that enables migration of Hadoop and Spark analytics workloads to AWS without the need for specialized skills. Learn how Cazena makes it easy to migrate to AWS while ensuring your data is as secure on the cloud as it is on-premises.
Successful data lake implementations can serve a corporation well for years. Accenture, an APN Premier Consulting Partner, recently had an engagement with a Fortune 500 company that wanted to optimize its AWS data lake implementation. As part of the engagement, Accenture moved the customer to better-suited services and developed metrics to closely monitor the health of its overall environment in the cloud.
Data and analytics success relies on providing analysts and data end users with quick, easy access to accurate, quality data. Enterprises need a high performing and cost-efficient data architecture that supports demand for data access, while providing the data governance and management capabilities required by IT. Data management excellence, which is best achieved via a data lake on AWS, captures and makes quality data available to analysts in a fast and cost-effective way.