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 Data Cleanse 52

Monte Carlo

FEBRUARY 22, 2021

Gopi Krishnamurthy, Director of Engineering, leads the team responsible for data engineering, infrastructure, cloud center-of-excellence, growth, and monetization. For Blinkist, having trustworthy and reliable data is foundational to the success of their business. Image courtesy of Blinkist. “At Image courtesy of Blinkist.

Data Pipeline 52

Data Pipeline Data Government Data Engineering 52

Precisely

MAY 13, 2024

It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format. Questions to ask each vendor: Does your data integration solution provide access to the metadata? What data governance controls do your solutions have in place?

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Databand.ai

JULY 19, 2023

However, their importance has grown significantly in recent years due to the increasing complexity of data architectures and the growing need for data governance and compliance. In this article: Why Are Data Lineage Tools Important? Atlan Atlan offers a modern approach to data governance.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

François Nguyen

JANUARY 18, 2021

To finish the trilogy (Dataops, MLops), let’s talk about DataGovOps or how you can support your Data Governance initiative. Last part, it was added the data security and privacy part. Every data governance policy about this topic must be read by a code to act in your data platform (access management, masking, etc.)

Data Governance 130

Data Governance Metadata Government Data Pipeline 130

Christophe Blefari

APRIL 12, 2024

In the same conference a lot of other talks took place here a few selection you should check out: How to pivot your data team from a service team to a value-generator — Very often data teams struggle in delivering value or in finding what's their real identity.

BI 130

BI SQL Data Coding 130

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How do we build data products ? How can we interoperate between the data domains ? We want interoperability for any data stored versus we have to think how to store the data in a specific node to optimize the processing.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Knowledge Hut

MARCH 20, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Data Engineering Weekly

MARCH 3, 2024

link] Zendesk: dbt at Zendesk The Zendesk team shares their journey of migrating legacy data pipelines to dbt, focusing on making them more reliable, efficient, and scalable. The article also highlights sink-specific improvements and operator-specific enhancements that contribute to the overall performance boost.

Data Engineering 67

Data Engineering Data Engineer Pipeline-centric Engineering 67

Knowledge Hut

MARCH 15, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Data Engineering Weekly

MARCH 17, 2024

[link] Jarkko Moilanen: Exploring the Frontier of Data Products - Seeking Insights on Emerging Standards Data products are becoming a central part of the data pipeline strategy, bringing a new approach to building data as an asset.

Data Engineering 70

Data Engineering Data Engineer Engineering Data Governance 70

Towards Data Science

JUNE 8, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Why is data quality ownership important? The governance team treats every team output as a data product.

Data Governance 79

Data Governance Government Generalist Data Engineering 79

Monte Carlo

JUNE 6, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Table of Contents Why is important to answer who is responsible for data quality?

Data Governance 98

Data Governance Government Data Data Engineering 98

Databand.ai

AUGUST 30, 2023

Poor data quality: The lack of automation and data governance in legacy architectures can lead to data quality issues, such as incomplete, inaccurate, or duplicate data. This may involve the use of data pipelines, data integration platforms, or data processing frameworks.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Databand.ai

AUGUST 30, 2023

Besides these categories, specialized solutions tailored specifically for particular domains or use cases also exist, such as ETL (Extract-Transform-Load) tools for managing data pipelines, data integration tools for combining information from disparate sources/systems, and more.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Ascend.io

OCTOBER 12, 2023

By evaluating the current state of your data ecosystem and establishing explicit objectives, you set the stage for a successful automation transition. Additionally, considerations around data governance and initial workflow design ensure that when you do move forward, you do so with confidence and direction.

Hospitality 52

Hospitality Data Pipeline Healthcare Data Governance 52

Ascend.io

JANUARY 10, 2024

This process ensures your data is accurate, complete, and consistently up-to-date — preventing any degradation that could mislead your AI, which can lead to erroneous outputs. Effective Data Governance: Lastly, let’s talk about governance. This is your rulebook for managing data. Actionable tip?

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Monte Carlo

JANUARY 16, 2024

Factor in the advertising strategies, media production, partner programming, audience analytics…and you’re looking at an ocean of data that would fill even the deepest trench (we’d like a television show about that too, please!). So how does Fox’s data strategy support these complex data workflows? Image from Castor.

Government 52

Government High Quality Data Data Governance Entertainment 52

Precisely

JANUARY 9, 2023

It enables business users to proactively address potential problems as they happen, resulting in healthier data pipelines, more productive teams, and happier customers. Many businesses now cite faster access to relevant data, and higher quality of data and insights, as being the top benefits of data governance initiatives.

Data Integration 81

Data Integration Data Governance Government Data 81

Monte Carlo

JANUARY 3, 2024

This is a widely shared sentiment across many data leaders I speak to. If the data team has suddenly surfaced customer-facing, secure data, then they’re on the hook. Data governance is a massive consideration and it’s a high bar to clear. RAG application architecture. It’s 2024 now.

Technology 113

Technology Database Data Governance Data Engineering 113

The Modern Data Company

JULY 31, 2023

By omitting the complexity and debt of data pipelines resulting from multiple fragmented tools in the ‘MAD Ecosystem,’ these loops can be efficiently maintained Process Angle : Administering Data Products involves a combination of technology, culture, and process.

Government 98

Government Data Governance Architecture Data Pipeline 98

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

The Modern Data Company

FEBRUARY 15, 2023

Limited resources: Data management has always been resource intensive, but not all organizations can maintain a full data team. Without suitable resources for company-wide data management, it’s easier to fall behind.

Government 98

Government Data Governance Raw Data Data 98

Databand.ai

AUGUST 30, 2023

Besides these categories, specialized solutions tailored specifically for particular domains or use cases also exist, such as extract, transform and load (ETL) tools for managing data pipelines, data integration tools for combining information from disparate sources or systems and more.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 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. This is known as data governance.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Data Engineering Podcast

JUNE 5, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done.

Data Security 100

Data Security Metadata MongoDB Scala 100

Ascend.io

DECEMBER 19, 2023

That’s what we call a data pipeline. It could just as well be ‘ELT for Snowflake’ The key takeaway is that these terms are representative of the actual activity being undertaken: the construction and management of data pipelines within the Snowflake environment.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Data Engineering Podcast

JULY 10, 2022

Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Ascend.io

SEPTEMBER 5, 2023

Data Governance: With Ascend, users can track various metrics such as byte consumption and record counts, as well as build lineage through the entire process, including Python and SQL. This enhances data governance and aids in decision-making. This paves the way for new interactions and capabilities.

IT 59

IT Scala Java Programming Language 59

Databand.ai

AUGUST 30, 2023

Measuring data integrity is more complex, as it involves assessing the consistency, reliability, and trustworthiness of data throughout its lifecycle. Methods While data accuracy and data integrity have similar objectives, the methods used to achieve them are different.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Databand.ai

JULY 19, 2023

Learn more in our detailed guide to the data ingestion process (coming soon) Best Practices for Effective Data Ingestion Defining Clear Data Governance Policies Data governance involves the overall management of data availability, usability, integrity, and security.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Databand.ai

AUGUST 30, 2023

This may include providing them with access to data quality tools and resources, as well as offering regular training sessions to help them stay up-to-date with the latest best practices in data management. Automate Lineage and Data Governance Data lineage tools can play a crucial role in automating your data quality strategy.

Building 52

Building Data Cleanse Data Governance Datasets 52

Monte Carlo

SEPTEMBER 27, 2023

Testing for data quality Data testing helps teams validate assumptions about data, systematically verifying data accuracy, completeness, and consistency. While data testing won’t cover every scenario, it will help you catch specific, known problems that can occur in your data pipelines.

Data Governance 52

Data Governance Government Data Data Pipeline 52

Monte Carlo

SEPTEMBER 27, 2023

We’re also excited to announce Monte Carlo’s participation in the Alation Open Data Quality Initiative , a program designed to give Alation customers the freedom to choose the data quality or observability solution of their choice to expand data trust in the Alation platform.

Data Governance 69

Data Governance Government Data Pipeline Data 69

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional who is in charge of designing, implementing, and maintaining data processing systems and solutions on the Microsoft Azure cloud platform. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Databand.ai

JULY 5, 2023

What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Data observability tools allow teams to detect issues such as missing values, duplicate records, or inconsistent formats early on before they affect downstream processes.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52