Data Pipeline, Data Validation and Raw Data

Data Pipeline

Data Validation

Raw Data

Data Engineering Weekly #165

Data Engineering Weekly

MARCH 31, 2024

The blog further emphasizes its increased investment in Data Mesh and clean data. link] Databricks: PySpark in 2023 - A Year in Review Can we safely say PySpark killed Scala-based data pipelines? I often noticed that the derived data is always > 10 times larger than the warehouse's raw data.

Data Engineering

Data Engineering Data Engineer Engineering Scala

Data testing tools: Key capabilities you should know

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools. This is part of a series of articles about data quality.

Data Cleanse

Data Cleanse Data Pipeline Datasets Data Validation

Join 16,000+

Insiders

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

Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

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

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

MORE WEBINARS

Trending Sources

Moving Past ETL and ELT: Understanding the EtLT Approach

Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. Their task is straightforward: take the raw data and transform it into a structured, coherent format.

Data Lake

Data Lake ETL Tools Data Warehouse Data Pipeline

Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

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

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

MORE WEBINARS

Data Testing Tools: Key Capabilities and 6 Tools You Should Know

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming, and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools. This is part of a series of articles about data quality.

Data Cleanse

Data Cleanse Data Validation Data Pipeline Datasets

What is ELT (Extract, Load, Transform)? A Beginner’s Guide [SQ]

Databand.ai

JULY 19, 2023

The Transform Phase During this phase, the data is prepared for analysis. This preparation can involve various operations such as cleaning, filtering, aggregating, and summarizing the data. The goal of the transformation is to convert the raw data into a format that’s easy to analyze and interpret.

Data Cleanse

Data Cleanse Raw Data Data Storage Data Warehouse

How to Design a Modern, Robust Data Ingestion Architecture

Monte Carlo

MAY 28, 2024

Data Loading : Load transformed data into the target system, such as a data warehouse or data lake. In batch processing, this occurs at scheduled intervals, whereas real-time processing involves continuous loading, maintaining up-to-date data availability. A typical data ingestion flow.

Data Ingestion

Data Ingestion Architecture Designing Hadoop

DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

AUGUST 30, 2023

This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management. These practices help ensure that the data being ingested is accurate, complete, and consistent across all sources.

Architecture

Architecture Data Ingestion Data Governance Data Cleanse

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

Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. The goal of this strategy is to streamline the entire process of extracting insights from raw data by removing silos between teams and technologies.

Data Governance

Data Governance Data Cleanse Government Data Pipeline

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

SEPTEMBER 21, 2023

This results in rallying 26 team members—likely the cream of the crop—to spend an entire day investigating the problem, only to discover that a single blank field passed through the data pipeline was the culprit. DataKitchen’s suite of products offers a robust implementation of these Five Pillars of Data Journeys.

Raw Data

Raw Data Data Business Intelligence High Quality Data

What is ETL Pipeline? Process, Considerations, and Examples

ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process

Process Data Pipeline Data Warehouse AWS

The Case for Automated ETL Pipelines

Ascend.io

SEPTEMBER 19, 2023

ETL stands for: Extract: Retrieve raw data from various sources. Transform: Process the data to make it suitable for analysis (this can involve cleaning, aggregating, enriching, and restructuring). This not only streamlines data management but also enhances the speed and accuracy of issue detection and resolution.

ETL Tools

ETL Tools ETL Method Utilities Data Engineering

What is a Data Source?

Grouparoo

NOVEMBER 29, 2021

Typically, such information may transition through a database or other data store for access as secondary data by the data processor. Machine Data Equipment ranging from simple sensors to complex operational technology may generate information as a data source. It may be raw data, validated data, or big data.

Raw Data

Raw Data Big Data Relational Database Data Warehouse

SQL for Data Engineering: Success Blueprint for Data Engineers

ProjectPro

FEBRUARY 16, 2023

Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. Your SQL skills as a data engineer are crucial for data modeling and analytics tasks. Making data accessible for querying is a common task for data engineers.

Data Engineering

Data Engineering Data Engineer SQL Engineering

100+ Big Data Interview Questions and Answers 2023

ProjectPro

JANUARY 31, 2023

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data

Big Data Hadoop AWS Relational Database

Data Engineering Digest

Data Engineering Weekly #165

Data testing tools: Key capabilities you should know

Webinars

Trending Sources

Moving Past ETL and ELT: Understanding the EtLT Approach

Webinars

Data Testing Tools: Key Capabilities and 6 Tools You Should Know

What is ELT (Extract, Load, Transform)? A Beginner’s Guide [SQ]

How to Design a Modern, Robust Data Ingestion Architecture

DataOps Architecture: 5 Key Components and How to Get Started

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

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

What is ETL Pipeline? Process, Considerations, and Examples

The Case for Automated ETL Pipelines

What is a Data Source?

SQL for Data Engineering: Success Blueprint for Data Engineers

100+ Big Data Interview Questions and Answers 2023

Stay Connected