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 110

Engineering Kafka Data Pipeline Data Warehouse 110

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

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 124

Consulting Machine Learning Data Science Data Pipeline 124

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

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

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 Data Pipeline Aggregated Data 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

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

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

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

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

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

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

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 49

Data Validation Data Data Pipeline Data Solutions 49

Striim

AUGUST 28, 2024

Advanced real-time analytics platforms incorporate robust data cleaning and anomaly detection, ensuring models receive high-quality, stable inputs. Tools like Apache Beam and Spark Streaming provide mechanisms for real-time data validation and cleansing.

Healthcare 96

Healthcare Retail Algorithm Finance 96

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

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

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

Airbnb Tech

NOVEMBER 28, 2023

There were several inputs that certainly could help us measure quality, but if they could not be automatically measured ( Automated ), or if they were so convoluted that data practitioners wouldn’t understand what the criterion meant or how it could be improved upon ( Actionable ), then they were discarded.

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

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

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

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

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

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

Monte Carlo

FEBRUARY 15, 2023

But, while this can provide some much-needed context, it doesn’t provide the granularity data teams need to remediate the data problems they uncover—or prevent them from happening again in the future. In the context of data pipelines, column level lineage traces the relationships across and between upstream source systems (i.e.,

Data Validation 52

Data Validation Data Data Engineering Data Engineer 52