Snapshot and Structured Data - Data Leaders Brief

Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

NOVEMBER 26, 2024

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. Querying all snapshots, we can see that we created three snapshots with overwrites after the initial one.

Metadata

Metadata Data Lake Snapshot Data Warehouse

Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift

AWS Big Data

JULY 28, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It is designed for analyzing large volumes of data and performing complex queries on structured and semi-structured data. Redshift resources, such as namespaces, workgroups, snapshots, and clusters can be tagged.

Snapshot

Snapshot Metadata Measurement Data Warehouse

Chose Both: Data Fabric and Data Lakehouse

Cloudera

SEPTEMBER 12, 2022

First, organizations have a tough time getting their arms around their data. More data is generated in ever wider varieties and in ever more locations. Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making. Better together.

Unstructured Data

Unstructured Data Data Architecture Data Lake Snapshot

Webinars

Automation, Evolved: Your New Playbook For Smarter Knowledge Work

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Empower Your Cyber Defenders with Real-Time Analytics

Cloudera

NOVEMBER 15, 2024

Using Cloudera Data Flow and Cloudera Stream Processing, teams can filter, parse, normalize, and enrich log data in real time, ensuring that defenders are always working with clean, structured data that’s ready for advanced analytics.

Analytics

Analytics Metadata Data-driven Snapshot

Exploring real-time streaming for generative AI Applications

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. This allows the model to adapt to the latest changes in price and availability. versions).

Data Lake

Data Lake Unstructured Data Management Snapshot

Ensuring Data Transformation Quality with dbt Core

Wayne Yaddow

MARCH 14, 2025

Snapshot testing augments debugging capabilities by recording past table states, facilitating the identification of unforeseen spikes, declines, or abnormalities before their effect on production systems.

Data Transformation

Data Transformation Testing Unstructured Data Data Quality

Implement slowly changing dimensions in a data lake using AWS Glue and Delta

AWS Big Data

MARCH 28, 2023

This post is designed to be implemented for a real customer use case, where you get full snapshot data on a daily basis. The dataset represents employee details such as ID, name, address, phone number, contractor or not, and more. Delete the stack from the AWS CloudFormation console.

Data Lake

Data Lake Testing Snapshot Big Data

Data Engineers Are Using AI to Verify Data Transformations

Wayne Yaddow

FEBRUARY 26, 2025

Data engineers may include AI-based schema detection technologies into their continuous integration and continuous delivery (CI/CD) pipelines to fix formatting issues before they worsen. This quick feedback loop is crucial for ensuring data dependability and reducing downtime.

Data Transformation

Data Transformation Testing Data-driven Data Quality

Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

JUNE 21, 2023

The challenge comes when we need to ask more complex questions of our data, for example, what was the year-on-year quarterly sales growth by product broken down by country? The case for a data warehouse A data warehouse is ideally suited to answer OLAP queries. To house our data, we need to define a data model.

Data Warehouse

Data Warehouse Data Lake OLAP Cost-Benefit

Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Corinium

APRIL 25, 2019

Then when there is a breach, it comes as a shock, “wow, I didn’t even know that application had access to so much sensitive data”. Step One in any data security program should first be to discover and classify datasets that are sensitive, and know where that data is, and understand who really needs it to do their jobs.

Insurance

Insurance Risk IoT Data-driven

Implement a serverless CDC process with Apache Iceberg using Amazon DynamoDB and Amazon Athena

AWS Big Data

AUGUST 16, 2023

Time travel Time travel queries in Athena query Amazon S3 for historical data from a consistent snapshot as of a specified date and time. Version travel queries in Athena query Amazon S3 for historical data as of a specified snapshot ID. Iceberg tables provide the capability of time travel.

Data Lake

Data Lake Metadata Testing Snapshot

Discover Efficient Data Extraction Through Replication With Angles Enterprise for Oracle

Jet Global

NOVEMBER 7, 2023

Advantages : Replication reduces the load on source systems because data extraction occurs at predefined intervals, reducing the real-time impact on production systems. It provides consistency in data for reporting purposes, as you are working with snapshots of the data at a particular point in time.

Enterprise

Enterprise Data Warehouse Operational Reporting Reporting

Empower Your Cyber Defenders with Real-Time Analytics Author: Carolyn Duby, Field CTO

Cloudera

NOVEMBER 15, 2024

Using Cloudera Data Flow and Cloudera Stream Processing, teams can filter, parse, normalize, and enrich log data in real time, ensuring that defenders are always working with clean, structured data that’s ready for advanced analytics.

Analytics

Analytics Metadata Data-driven Snapshot

Accelerate queries on Apache Iceberg tables through AWS Glue auto compaction

AWS Big Data

DECEMBER 19, 2024

Data lakes were originally designed to store large volumes of raw, unstructured, or semi-structured data at a low cost, primarily serving big data and analytics use cases. Announced during AWS re:Invent 2023, this feature focuses on optimizing data storage for Iceberg tables using the CoW mechanism.

Data Lake

Data Lake IoT Metadata Testing

Data Leaders Brief

Run Apache XTable in AWS Lambda for background conversion of open table formats

Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift

Webinars

Trending Sources

Chose Both: Data Fabric and Data Lakehouse

Webinars

Empower Your Cyber Defenders with Real-Time Analytics

Exploring real-time streaming for generative AI Applications

Ensuring Data Transformation Quality with dbt Core

Implement slowly changing dimensions in a data lake using AWS Glue and Delta

Data Engineers Are Using AI to Verify Data Transformations

Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Implement a serverless CDC process with Apache Iceberg using Amazon DynamoDB and Amazon Athena

Discover Efficient Data Extraction Through Replication With Angles Enterprise for Oracle

Empower Your Cyber Defenders with Real-Time Analytics Author: Carolyn Duby, Field CTO

Accelerate queries on Apache Iceberg tables through AWS Glue auto compaction

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