Striim

APRIL 18, 2025

How Organizations Can Overcome Data Quality and Availability Challenges Many businesses are shifting toward real-time data pipelines to ensure their AI and analytics strategies are built on reliable information. Enabling AI & ML with Adaptive Data Pipelines AI models require ongoing updates to stay relevant.

High Quality Data 52

High Quality Data Business Intelligence Unstructured Data Data Pipeline 52

Monte Carlo

MARCH 24, 2023

The data doesn’t accurately represent the real heights of the animals, so it lacks validity. Let’s dive deeper into these two crucial concepts, both essential for maintaining high-quality data. Let’s dive deeper into these two crucial concepts, both essential for maintaining high-quality data. What Is Data Validity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Data Engineering Weekly

FEBRUARY 2, 2025

[link] Atlassian: Lithium - elevating ETL with ephemeral and self-hosted pipelines The article introduces Lithium, an ETL++ platform developed by Atlassian for dynamic and ephemeral data pipelines, addressing unique needs like user-initiated migrations and scheduled backups. million entities per second in production.

Data Engineering 58

Data Engineering Data Engineer Engineering Data Lake 58

Edureka

APRIL 22, 2025

Data Quality and Governance In 2025, there will also be more attention paid to data quality and control. Companies now know that bad data quality leads to bad analytics and, ultimately, bad business strategies. Companies all over the world will keep checking that they are following global data security rules like GDPR.

Data Engineering 52

Data Engineering Data Engineer Engineering Consulting 52

Ascend.io

APRIL 27, 2023

Going into the Data Pipeline Automation Summit 2023, we were thrilled to connect with our customers and partners and share the innovations we’ve been working on at Ascend. The summit explored the future of data pipeline automation and the endless possibilities it presents.

Data Pipeline 52

Data Pipeline Pipeline-centric Data Validation Data Engineering 52

Acceldata

DECEMBER 5, 2022

ValidationLearn how a data observability solution can automatically clean and validate incoming data pipelines in real-time.

Data Validation 52

Data Validation Data Pipeline Data 52

Snowflake

AUGUST 24, 2023

.” homegenius’ data challenges homegenius’ data engineering team had three big data challenges it needed to solve, according to Goodrich. The data science team needed data transformations to happen quicker, the quality of data validations to be better, and the turnaround time for pipeline testing to be faster.

Data Pipeline 52

Data Pipeline Python Programming Language Data Validation 52

Data Engineering Weekly

MAY 16, 2023

I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.

Engineering 111

Engineering Kafka Data Pipeline Data Warehouse 111

Striim

JANUARY 17, 2025

To achieve accurate and reliable results, businesses need to ensure their data is clean, consistent, and relevant. This proves especially difficult when dealing with large volumes of high-velocity data from various sources. Sherlock monitors your data streams to identify sensitive information.

Healthcare 52

Healthcare Google Cloud Government Data Validation 52

DataKitchen

APRIL 13, 2021

Airflow — An open-source platform to programmatically author, schedule, and monitor data pipelines. DBT (Data Build Tool) — A command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively. Soda Data Monitoring — Soda tells you which data is worth fixing.

Consulting 121

Consulting Machine Learning Data Science Data Pipeline 121

Data Engineering Podcast

MAY 26, 2024

Starburst Logo]([link] This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake.

Systems 130

Systems Data Lake High Quality Data Google Cloud 130

Monte Carlo

NOVEMBER 14, 2024

Bad data can infiltrate at any point in the data lifecycle, so this end-to-end monitoring helps ensure there are no coverage gaps and even accelerates incident resolution. Data and data pipelines are constantly evolving and so data quality monitoring must as well,” said Lior.

Relational Database 52

Relational Database SQL Metadata Data Validation 52

Data Engineering Podcast

SEPTEMBER 25, 2022

Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance.

Building 100

Building Metadata MongoDB MySQL 100

Wayne Yaddow

MARCH 3, 2025

These strategies can prevent delayed discovery of quality issues during data observability monitoring in production. Below is a summary of recommendations for proactively identifying and fixing flaws before they impact production data. Saves time by automating routine validation tasks and preventing costly downstream errors.

Data Pipeline 52

Data Pipeline Data Validation Data Datasets 52

Databand.ai

AUGUST 30, 2023

Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively. Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. In this article: Why Are DataOps Tools Important?

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

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 52

Data Cleanse Data Validation Data Pipeline Data Governance 52

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 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Wayne Yaddow

FEBRUARY 18, 2025

Selecting the strategies and tools for validating data transformations and data conversions in your data pipelines. Introduction Data transformations and data conversions are crucial to ensure that raw data is organized, processed, and ready for useful analysis.

Data Pipeline 52

Data Pipeline SQL Raw Data Python 52

Data Engineering Weekly

APRIL 30, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make collecting data from every application, website, and SaaS platform easy, then activating it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineering 63

Data Engineering Data Engineer Engineering PostgreSQL 63

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.

Unstructured Data 52

Unstructured Data SQL Data Pipeline Data Validation 52

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? The Netflix blog emphasizes the importance of finding the zombie data and the system design around deleting unused data.

Data Engineering 63

Data Engineering Data Engineer Engineering Scala 63

Databand.ai

JULY 19, 2023

Proper Planning and Designing of the Data Pipeline The first step towards successful ELT implementation is proper planning and design of the data pipeline. This involves understanding the business requirements, the source and type of data, the desired output, and the resources required for the ELT process.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Databand.ai

AUGUST 30, 2023

It emphasizes the importance of collaboration between different teams, such as data engineers, data scientists, and business analysts, to ensure that everyone has access to the right data at the right time. This includes data ingestion, processing, storage, and analysis.

Data Governance 52

Data Governance Data Pipeline Government Business Analyst 52

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. Let’s take a closer look. Enterprises have an opportunity to undergo a metamorphosis.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

dbt Developer Hub

APRIL 24, 2023

Alteryx is a visual data transformation platform with a user-friendly interface and drag-and-drop tools. Nonetheless, Alteryx may have difficulties to cope with the complexity increase within an organization’s data pipeline, and it can become a suboptimal tool when companies start dealing with large and complex data transformations.

BI 83

BI Data Workflow SQL Data Pipeline 83

Wayne Yaddow

FEBRUARY 26, 2025

Photo by Markus Spiske on Unsplash Introduction Senior data engineers and data scientists are increasingly incorporating artificial intelligence (AI) and machine learning (ML) into data validation procedures to increase the quality, efficiency, and scalability of data transformations and conversions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Monte Carlo

JANUARY 10, 2024

In this article, we present six intrinsic data quality techniques that serve as both compass and map in the quest to refine the inner beauty of your data. Data Profiling 2. Data Cleansing 3. Data Validation 4. Data Auditing 5. Data Governance 6. Table of Contents 1.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Databand.ai

AUGUST 30, 2023

Accurate data ensures that these decisions and strategies are based on a solid foundation, minimizing the risk of negative consequences resulting from poor data quality. There are various ways to ensure data accuracy. Data cleansing involves identifying and correcting errors, inconsistencies, and inaccuracies in data sets.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Grouparoo

OCTOBER 6, 2021

Themes I was drawn to the articles that speak to a theme in the data world that I am passionate about: how data pipelines and data team practices are evolving to be more like traditional product development. 7 Be Intentional About the Batching Model in Your Data Pipelines Different batching models.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

RandomTrees

FEBRUARY 2, 2024

GPT-Based Data Engineering Accelerators: Given below is the list of some of the GPT-based data engineering accelerators. 1. DataGPT OpenAI developed DataGpt for performing data engineering tasks. Datagpt creates code for data pipelines and transformations.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Databand.ai

AUGUST 30, 2023

A shorter time-to-value indicates that your organization is efficient at processing and analyzing data for decision-making purposes. Monitoring this metric helps identify bottlenecks in the data pipeline and ensures timely insights are available for business users.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Databand.ai

AUGUST 30, 2023

It plays a critical role in ensuring that users of the data can trust the information they are accessing. There are several ways to ensure data consistency, including implementing data validation rules, using data standardization techniques, and employing data synchronization processes.

Data Integration 52

Data Integration Data Cleanse Data Validation High Quality Data 52

Databand.ai

JULY 19, 2023

Despite these challenges, proper data acquisition is essential to ensure the data’s integrity and usefulness. Data Validation In this phase, the data that has been acquired is checked for accuracy and consistency.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Monte Carlo

SEPTEMBER 12, 2024

Validity rules & dimension drift Writing rules for accepted values for low cardinality fields and data validity can be tedious. Coverage for ‘unknown unknown’ issues are some of the most important monitors for data teams to create—and they’re also the checks that most often get missed. No rules required.

Data 80

Data Machine Learning Data Validation Algorithm 80

dbt Developer Hub

APRIL 30, 2023

My next gig was in consulting where I bootstrapped my way into data engineering and had to learn the whole gamut below. But in practice, I was babysitting brittle data pipelines. To enable dozens of people was mind-numbing, much less hundreds of data analysts to all work elegantly together. 5x data engineer.

Data Engineering 64

Data Engineering Data Engineer Engineering Finance 64

Knowledge Hut

APRIL 19, 2023

Use modular design: Divide the pipeline into small individual parts that can be independently tested and adjusted. Data validation: Data validation as it goes through the pipeline to ensure it meets the necessary quality standards and is appropriate for the final goal.

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52