Data Integration, Data Workflow and Metadata

Data Integration

Data Workflow

Metadata

3. Psyberg: Automated end to end catch up

Netflix Tech

NOVEMBER 14, 2023

Input : List of source tables and required processing mode Output : Psyberg identifies new events that have occurred since the last high watermark (HWM) and records them in the session metadata table. The session metadata table can then be read to determine the pipeline input. Audit Run various quality checks on the staged data.

Metadata

Metadata Data Pipeline Scala Data Workflow

Data Catalog - A Broken Promise

Data Engineering Weekly

DECEMBER 29, 2022

Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. It makes rolling out the data catalogs.

Metadata

Metadata Data Warehouse ETL Tools Data Workflow

Join 16,000+

Insiders

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Webinars

Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

How To Get Promoted In Product Management

MORE WEBINARS

Trending Sources

Unleashing the Power of CDC With Snowflake

Workfall

JUNE 12, 2023

It ensures that organisations stay at the forefront by capturing every twist and turn in the data landscape. With CDC by their side, organisations unlock the power of informed decision-making, safeguard data integrity, and enable lightning-fast analytics. CDC also plays a crucial role in data integration and ETL processes.

Telecommunication

Telecommunication Metadata Healthcare Finance

Webinars

Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

How To Get Promoted In Product Management

MORE WEBINARS

DataOps Tools: Key Capabilities & 5 Tools You Must Know About

Databand.ai

AUGUST 30, 2023

By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes. DataOps tools can be categorized into several types, including data integration tools, data quality tools, data catalog tools, data orchestration tools, and data monitoring tools.

Data Cleanse

Data Cleanse Data Pipeline Data Ingestion Data Validation

DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture

Architecture Data Ingestion Data Governance Data Cleanse

Data Orchestration: Defining, Understanding, and Applying

Ascend.io

DECEMBER 11, 2023

Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. So, why is data orchestration a big deal? It automates and optimizes data processes, reducing manual effort and the likelihood of errors.

Data Workflow

Data Workflow Data Pipeline Data Lake Data

Data Engineering Weekly Radio #120

Data Engineering Weekly

MARCH 11, 2023

[link] Data Engineering Weekly Data Catalog - A Broken Promise Data catalogs are the most expensive data integration systems you never intended to build.

Data Engineering

Data Engineering Data Engineer Engineering High Quality Data

Unified DataOps: Components, Challenges, and How to Get Started

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. This demands the implementation of advanced data integration techniques, such as real-time streaming ingestion, batch processing, and API-based access.

Data Governance

Data Governance Data Cleanse Government Data Pipeline

Three Takeaways from Gartner’s 2019 Magic Quadrant for Data Management Solutions for Analytics

Cloudera

FEBRUARY 11, 2019

Cloudera provides a unified platform with multiple data apps and tools, big data management, hybrid cloud deployment flexibility, admin tools for platform provisioning and control, and a shared data experience for centralized security, governance, and metadata management. 3. Expansion beyond core data management.

Data Management

Data Management Management Metadata Government

The Good and the Bad of Apache Airflow Pipeline Orchestration

AltexSoft

NOVEMBER 7, 2022

DevOps tasks — for example, creating scheduled backups and restoring data from them. Airflow is especially useful for orchestrating Big Data workflows. There are typically hundreds of workflows in the Airflow production environment. Metadata database. The most common applications of the platform are.

PostgreSQL

PostgreSQL Metadata Python MySQL

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

MARCH 14, 2023

It must collect, analyze, and leverage large amounts of customer data from various sources, including booking history from a CRM system, search queries tracked with Google Analytics, and social media interactions. Okay, data lives everywhere, and that’s the problem the second component solves.

IT Data Warehouse Data Governance Data Lake

Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

Netflix Tech

OCTOBER 27, 2020

Netflix Scheduler is built on top of Meson which is a general purpose workflow orchestration and scheduling framework to execute and manage the lifecycle of the data workflow. Bulldozer makes data warehouse tables more accessible to different microservices and reduces each individual team’s burden to build their own solutions.

Data Warehouse

Data Warehouse Datasets Data Big Data

What Is Data Pipeline Automation?

Ascend.io

MARCH 17, 2023

For data pipeline automation, these previous approaches have foundered on one important barrier: the need to scale. This can be done with unique digital fingerprints that map not just every snippet of data, but the data engineers’ code as well. Let’s outline what these capabilities should look like.

Data Pipeline

Data Pipeline Datasets Data Software Engineer