Remove Data Analytics Remove Data Lake Remove Optimization Remove Unstructured Data
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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. Apache Iceberg integration is supported by AWS analytics services including Amazon EMR , Amazon Athena , and AWS Glue. AWS Glue 3.0

Data Lake 121
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Understanding Structured and Unstructured Data

Sisense

Different types of information are more suited to being stored in a structured or unstructured format. Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Unstructured data.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

This data store provides your organization with the holistic customer records view that is needed for operational efficiency of RAG-based generative AI applications. For building such a data store, an unstructured data store would be best. This is typically unstructured data and is updated in a non-incremental fashion.

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Data architecture strategy for data quality

IBM Big Data Hub

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

This is the first post to a blog series that offers common architectural patterns in building real-time data streaming infrastructures using Kinesis Data Streams for a wide range of use cases. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

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