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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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Modern Data Architecture for Telecommunications

Cloudera

Data has continued to grow both in scale and in importance through this period, and today telecommunications companies are increasingly seeing data architecture as an independent organizational challenge, not merely an item on an IT checklist. Previously, there were three types of data structures in telco: .

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

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Eight Top DataOps Trends for 2022

DataKitchen

As a result, enterprises will examine their end-to-end data operations and analytics creation workflows. Instead of allowing technology to be a barrier to teamwork, leading data organizations in 2022 will further expand the automation of workflows to improve and facilitate communication and coordination between the groups.

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Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

Each data producer within the organization has its own data lake in Apache Hudi format, ensuring data sovereignty and autonomy. These datasets are pivotal for reporting and analytics use cases, powered by services like Amazon Redshift and tools like Power BI.