AWS Big Data

MARCH 10, 2023

They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. These transactional data lakes combine features from both the data lake and the data warehouse. Data can be organized into three different zones, as shown in the following figure.

Data Lake 122

Data Lake Sales Data Warehouse Snapshot 122

Cloudera

AUGUST 21, 2024

The root of the problem comes down to trusted data. Pockets and siloes of disparate data can accumulate across an enterprise or legacy data warehouses may not be equipped to properly manage a sea of structured and unstructured data at scale. Open Data Lakehouse also offers expanded support for Python 3.10

Snapshot 70

Snapshot Unstructured Data Data Architecture Data Warehouse 70

Cloudera

OCTOBER 11, 2022

The takeaway – businesses need control over all their data in order to achieve AI at scale and digital business transformation. The challenge for AI is how to do data in all its complexity – volume, variety, velocity. But it isn’t just aggregating data for models. Data needs to be prepared and analyzed.

Snapshot 100

Snapshot Data Science Data Warehouse Metadata 100

AWS Big Data

OCTOBER 18, 2023

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. The decoupled compute and storage architecture of Amazon Redshift enables you to build highly scalable, resilient, and cost-effective workloads.

Analytics 111

Analytics Data Warehouse Dashboards Testing 111

AWS Big Data

JANUARY 8, 2024

Kinesis Data Streams has native integrations with other AWS services such as AWS Glue and Amazon EventBridge to build real-time streaming applications on AWS. Refer to Amazon Kinesis Data Streams integrations for additional details. State snapshot in Amazon S3 – You can store the state snapshot in Amazon S3 for tracking.

Analytics 134

Analytics IoT Data-driven Snapshot 134

Cloudera

SEPTEMBER 12, 2022

Combining and analyzing both structured and unstructured data is a whole new challenge to come to grips with, let alone doing so across different infrastructures. Both obstacles can be overcome using modern data architectures, specifically data fabric and data lakehouse. Unified data fabric.

Unstructured Data 80

Unstructured Data Data Architecture Data Lake Snapshot 80

Cloudera

DECEMBER 3, 2024

Apache Iceberg, together with the REST Catalog, dramatically simplifies the enterprise data architecture, reducing the Time to Value, Time to Market, and overall TCO, and driving greater ROI. It provides real time metadata access by directly integrating with the Iceberg-compatible metastore.

Metadata 89

Metadata Data Warehouse ROI Snapshot 89

AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. Table data storage mode – There are two options: Historical – This table in the data lake stores historical updates to records (always append).

Data Lake 111

Data Lake Data Processing Metadata Snapshot 111

AWS Big Data

MARCH 25, 2024

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. Data streaming enables you to ingest data from a variety of databases across various systems.

Data Lake 119

Data Lake Unstructured Data Management Snapshot 119

AWS Big Data

FEBRUARY 9, 2023

To achieve this, they combine their CRM data with a wealth of information already available in their data warehouse, enterprise systems, or other software as a service (SaaS) applications. One widely used approach is getting the CRM data into your data warehouse and keeping it up to date through frequent data synchronization.

Data Warehouse 102

Data Warehouse Data-driven Snapshot Testing 102

AWS Big Data

JUNE 9, 2023

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. Clustering data for better data colocation using z-ordering.

Data Lake 130

Data Lake Metadata Statistics Optimization 130

AWS Big Data

DECEMBER 4, 2024

While traditional extract, transform, and load (ETL) processes have long been a staple of data integration due to its flexibility, for common use cases such as replication and ingestion, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures.

Data Integration 113

Data Integration Data Lake Statistics Data-driven 113

AWS Big Data

MARCH 28, 2023

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. This post is designed to be implemented for a real customer use case, where you get full snapshot data on a daily basis.

Data Lake 97

Data Lake Testing Snapshot Sales 97

AWS Big Data

MARCH 3, 2023

For more information, refer to Creating external tables for data managed in Delta Lake. A Delta table manifest contains a list of files that make up a consistent snapshot of the Delta table. You could use this high-level architecture for any other use cases where you need to use the latest version of Spark on EMR Serverless.

Data Lake 98

Data Lake Dashboards Metrics Metadata 98