Data Transformation, Snapshot and Software

Data Transformation

Snapshot

Software

MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. All ML projects are software projects.

IT Testing Experimentation Software

Ensuring Data Transformation Quality with dbt Core

Wayne Yaddow

MARCH 14, 2025

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

Data Transformation

Data Transformation Testing Unstructured Data Data Quality

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Webinars

How to Achieve High-Accuracy Results When Using LLMs

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Trending Sources

10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

MAY 2, 2023

Your Chance: Want to test a professional logistics analytics software? Use our 14-days free trial today & transform your supply chain! Your Chance: Want to test a professional logistics analytics software? Use our 14-days free trial today & transform your supply chain! Now’s the time to strike.

Big Data

Big Data Internet of Things Cost-Benefit Optimization

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

AUGUST 8, 2022

In June 2022, Cloudera announced the general availability of Apache Iceberg in the Cloudera Data Platform (CDP). Iceberg is a 100% open-table format, developed through the Apache Software Foundation , which helps users avoid vendor lock-in and implement an open lakehouse. . Now we have data as of the year 2006 also in the table.

Snapshot

Snapshot Data Warehouse Machine Learning Cost-Benefit

End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

JULY 26, 2023

To tackle these asks, this post defines the development lifecycle for data integration and demonstrates how software engineers and data engineers can design an end-to-end development lifecycle using AWS Glue, including development, testing, and CI/CD, using a sample baseline template.

Data Integration

Data Integration Snapshot Testing Visualization

Migrate Amazon Redshift from DC2 to RA3 to accommodate increasing data volumes and analytics demands

AWS Big Data

AUGUST 9, 2024

We carried out the migration as follows: We created a new cluster with eight ra3.4xlarge nodes from the snapshot of our four-node dc2.8xlarge cluster. TB of data. We turned off our internal ETL and ELT orchestrator, to prevent our data from being updated during the migration period.

Data Lake

Data Lake Analytics Data Warehouse Data-driven

How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. For users that require a unified view of software quality, this is unacceptable.

Software

Software Data Lake Testing Cost-Benefit

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

AWS Big Data

JULY 20, 2023

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering.

Data Lake

Data Lake Analytics Snapshot Data Quality

Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

JANUARY 17, 2024

The following are some highlighted steps: Run a snapshot query. %%sql You also can use transactional data lake features such as running snapshot queries, incremental queries, time travel, and DML query. He is deeply passionate about applying ML/DL and big data techniques to solve real-world problems.

Data Lake

Data Lake Snapshot Big Data Data-driven

Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake

Data Lake Management Metrics Data Warehouse

Build a data lake with Apache Flink on Amazon EMR

AWS Big Data

JANUARY 27, 2023

The Amazon EMR Flink CDC connector reads the binlog data and processes the data. Transformed data can be stored in Amazon S3. We use the AWS Glue Data Catalog to store the metadata such as table schema and table location. Refer to Create bootstrap actions to install additional software to run a bootstrap script.

Data Lake

Data Lake Metadata Business Analysis Data-driven

Streamline AWS WAF log analysis with Apache Iceberg and Amazon Data Firehose

AWS Big Data

FEBRUARY 18, 2025

These include managing complex extract, transform, and load (ETL) processes, handling schema validation, providing reliable delivery, and maintaining custom code for data transformations. Firehose delivers streaming data with configurable buffering options that can be optimized for near-zero latency.

Snapshot

Snapshot Optimization Data Lake Metadata

“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

JANUARY 23, 2023

Data lineage is typically stored in separate systems from the data itself and can be difficult to keep up-to-date. Five on DataOps Observability : DataOps Observability is the ability to understand the state and behavior of data and the software and hardware that carries and transforms it as it flows through systems.

Testing

Testing Data Governance Data Quality Data-driven

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

Data Leaders Brief

MLOps and DevOps: Why Data Makes It Different

Ensuring Data Transformation Quality with dbt Core

Webinars

Trending Sources

10 Examples of How Big Data in Logistics Can Transform The Supply Chain

Webinars

How to Use Apache Iceberg in CDP’s Open Lakehouse

End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

Migrate Amazon Redshift from DC2 to RA3 to accommodate increasing data volumes and analytics demands

How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

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

Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

Build a data lake with Apache Flink on Amazon EMR

Streamline AWS WAF log analysis with Apache Iceberg and Amazon Data Firehose

“You Complete Me,” said Data Lineage to DataOps Observability.

Discover Efficient Data Extraction Through Replication With Angles Enterprise for Oracle

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