Christophe Blefari

MARCH 15, 2024

Understand how BigQuery inserts, deletes and updates — Once again Vu took time to deep dive into BigQuery internal, this time to explain how data management is done. Pandera, a data validation library for dataframes, now supports Polars. It's inspirational.

Metadata 272

Metadata Datasets Data Data Warehouse 272

Ascend.io

OCTOBER 28, 2024

It is important to note that normalization often overlaps with the data cleaning process, as it helps to ensure consistency in data formats, particularly when dealing with different sources or inconsistent units. Data Validation Data validation ensures that the data meets specific criteria before processing.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 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 Datasets 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

Christophe Blefari

FEBRUARY 18, 2023

Decrease ETL costs with Apache Arrow — I've often written data extraction with pandas by doing pd.read_sql because it's super handy and you can have something that works quickly, but the cost in memory can be high. Benn thinks about the role of a data team in the business decisional journey.

Software Engineer 100

Software Engineer Software Engineering Data Validation Data 100

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 110

Engineering Kafka Data Pipeline Data Warehouse 110

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

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 Pipeline 52

Data Pipeline Data Governance 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 ETL Tools Data Warehouse Data Pipeline 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 62

Data Engineering Data Engineer Engineering Scala 62

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 Raw Data Data Storage Data Warehouse 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

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

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

Monte Carlo

OCTOBER 10, 2024

Attention to Detail : Critical for identifying data anomalies. Tools : Familiarity with data validation tools, data wrangling tools like Pandas , and platforms such as AWS , Google Cloud , or Azure. Data observability tools: Monte Carlo ETL Tools : Extract, Transform, Load (e.g., Informatica , Talend ).

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Precisely

AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies.

Data Governance 52

Data Governance Government High Quality Data Datasets 52

DataKitchen

FEBRUARY 23, 2024

Webinar: Beyond Data Observability: Personalization DataKitchen DataOps Observability Problem Statement White Paper: ‘Taming Chaos’ Technical Product Overview Four-minute online demo Detailed Product: Documentation Webinar: Data Observability Demo Day DataKitchen DataOps TestGen Problem Statement White Paper: ‘Mystery Box Full Of Data Errors’ (..)

Data Validation 47

Data Validation Data Data Pipeline Data Solutions 47

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

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

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 62

Data Engineering Data Engineer Engineering PostgreSQL 62

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

Monte Carlo

JUNE 20, 2024

The quality of the input affects the quality of the output – and in order for data teams to produce high-quality data products, they need high-quality data from the very start. But, in reality, a data pipeline is more nuanced than just the quality of your internal or external data sources.

High Quality Data 52

High Quality Data Machine Learning Algorithm Datasets 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

OCTOBER 2, 2024

Here is a list of the most popular tools for data lineage in Python: OpenLineage and Marquez : OpenLineage is an open framework for data lineage collection and analysis. Together, they are the most popular standardized way to collect, store, and analyze metadata about data pipelines, across various tools and systems.

Python 52

Python Datasets Metadata Data 52

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 52

Architecture Data Ingestion Data Governance Data Cleanse 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

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

Databand.ai

JULY 11, 2023

Data Cleansing Data cleansing, also known as data scrubbing or data cleaning, is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data. This functionality is critical for not only fixing current issues but also preventing future ones.

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

Databand.ai

AUGUST 30, 2023

In this article: Why Is Data Testing Important? It’s also important during data migration and integration projects, where data is moved or transformed and must maintain its integrity. Data Validation Testing Data validation testing ensures that the data entered into the system meets the predefined rules and requirements.

Data Validation 52

Data Validation Data Integration Data Database 52

Monte Carlo

APRIL 1, 2024

Thankfully, there are many automated solutions out there that can help ease the process by either setting up a routine or managing ongoing data monitoring. Data observability tools are the best way to maintain clean, accurate, and consistent data pipelines.

High Quality Data 52

High Quality Data Datasets Data Data Pipeline 52

Databand.ai

JULY 3, 2023

The value of that trust is why more and more companies are introducing Chief Data Officers – with the number doubling among the top publicly traded companies between 2019 and 2021, according to PwC. In this article: Why is data reliability important? Note that data validity is sometimes considered a part of data reliability.

Data Validation 52

Data Validation Data Collection Data Machine Learning 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

Databand.ai

AUGUST 30, 2023

Organizations need to automate various aspects of their data operations, including data integration, data quality, and data analytics. Test and Validate Lastly, organizations need to test and validate their unified DataOps implementation to ensure that it is delivering the desired outcomes.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

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

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52