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

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

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

The Modern Data Company

FEBRUARY 15, 2023

Maintaining a clear strategy for gathering, storing, and labeling data is challenging. 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

Knowledge Hut

MARCH 28, 2024

Data Engineer Career: Overview Currently, with the enormous growth in the volume, variety, and veracity of data generated and the will of large firms to store and analyze their data, data management is a critical aspect of data science. That’s where data engineers are on the go.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

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

Ascend.io

JANUARY 10, 2024

Visual representation of Conway’s Law ( source ) Read More: The Chief AI Officer: Avoid The Trap of Conway’s Law Process: Ensuring Data Readiness The backbone of successful AI implementation is robust data management processes. AI models are only as good as the data they consume, making continuous data readiness crucial.

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Data Engineering Podcast

JUNE 5, 2022

In this episode Sean Falconer explains the idea of a data privacy vault and how this new architectural element can drastically reduce the potential for making a mistake with how you manage regulated or personally identifiable information. 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

Data Engineering Podcast

JULY 10, 2022

They also discuss how they have established a guild system for training and supporting data professionals in the organization. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Tired of deploying bad data? Need to automate data pipelines with less red tape?

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. By automating data lineage, you can save time and resources, and reduce the risk of human error.

Building 52

Building Data Cleanse Data Governance Datasets 52

Precisely

JANUARY 9, 2023

Read the Report Improving Data Integrity and Trust through Transparency and Enrichment Read this report to learn how organizations are responding to trending topics in data integrity. 2023 will continue to see a major shift in organizations increasing their investment in business-first data governance programs.

Data Integration 81

Data Integration Data Governance Government Data 81

Ascend.io

SEPTEMBER 5, 2023

In the fast-evolving landscape of cloud data solutions, Snowflake has consistently been at the forefront of innovation, offering enterprises sophisticated tools to optimize their data management. 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

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

JULY 12, 2021

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. RudderStack’s smart customer data pipeline is warehouse-first.

Designing 100

Designing Data Warehouse Data Governance Consulting 100

The Modern Data Company

JULY 31, 2023

Achieving such flexible loops requires interoperable code, declarative change management, and independent infra provisioning (via IaC), akin to Software 101. Unified governance, particularly challenging in fragmented data stacks with numerous tools and data assets, is addressed through the infrastructure piece of Data Products.

Government 98

Government Data Governance Architecture Data Pipeline 98

Data Engineering Podcast

FEBRUARY 24, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

IT 100

IT Data Lake Data Pipeline Media 100

Data Engineering Podcast

APRIL 26, 2021

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

Business Intelligence 100

Business Intelligence Data Warehouse Hadoop Data Pipeline 100

Data Engineering Podcast

APRIL 5, 2021

She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 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

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

Data Engineering Podcast

OCTOBER 15, 2021

They also share their ambitions for the near future of adding data observability and data quality management features. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Metadata 100

Metadata BI Data Warehouse Government 100

Data Engineering Podcast

NOVEMBER 23, 2020

Summary One of the oldest aphorisms about data is "garbage in, garbage out", which is why the current boom in data quality solutions is no surprise. If you are wondering how to get better control of your own pipelines and the traps to avoid then this episode is definitely worth a listen. What are alternatives to Bigeye?

Hadoop 100

Hadoop Data Pipeline BI Data 100

Monte Carlo

JULY 13, 2023

In this article, we will explore how automation can help data engineering teams overcome these challenges and provide a more seamless and effective data management process. However, to appropriately grant access to the data, data engineers need to know why the users need access to the data and when this access should be revoked.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 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

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

Data Engineering Podcast

DECEMBER 14, 2020

This was a great conversation about real world experiences in building a successful data-oriented business. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering?

Data Analytics 100

Data Analytics Building MySQL Data 100

Cloudera

DECEMBER 1, 2022

Data observability provides insight into the condition and evolution of the data resources from source through the delivery of the data products. Barr Moses of Monte Carlo presents it as a combination of data flow, data quality, data governance, and data lineage.

Data Lake 81

Data Lake Data Pipeline Data Governance Analytics Application 81

AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. Bad data management be like, Source: Makeameme Data architects are sometimes confused with other roles inside the data science team.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Data Engineering Podcast

NOVEMBER 9, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Building 100

Building PostgreSQL BI Metadata 100

Data Engineering Podcast

OCTOBER 19, 2020

They also discuss methods for gaining visibility into the flow of data through your infrastructure, how to diagnose and prevent potential problems, and what they are building at Monte Carlo to help you maintain your data’s uptime. If you hand a book to a new data engineer, what wisdom would you add to it?

Machine Learning 100

Machine Learning Data Engineering Data Engineer Data 100

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

Monte Carlo

JUNE 16, 2023

Data integrity and quality may seem similar at first glance, and they are sometimes used interchangeably in everyday life, but they play unique roles in successful data management. Impact Now that you understand the purpose of data integrity and data quality, what is their impact on data management and decision-making?

Data Integration 52

Data Integration Hospitality Banking Healthcare 52

Data Engineering Podcast

NOVEMBER 30, 2020

If you are struggling with streaming data integration and change data capture then this interview is definitely worth a listen. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering?

Data Integration 100

Data Integration Coding BI Kafka 100

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

The Modern Data Company

JULY 31, 2023

Achieving such flexible loops requires interoperable code, declarative change management, and independent infra provisioning (via IaC), akin to Software 101. By omitting the complexity and debt of data pipelines resulting from multiple fragmented tools in the ‘MAD Ecosystem,’ these loops can be efficiently maintained.

Government 52

Government Data Governance Architecture Data Pipeline 52

The Modern Data Company

JULY 31, 2023

Achieving such flexible loops requires interoperable code, declarative change management, and independent infra provisioning (via IaC), akin to Software 101. By omitting the complexity and debt of data pipelines resulting from multiple fragmented tools in the ‘MAD Ecosystem,’ these loops can be efficiently maintained.

Government 52

Government Data Governance Architecture Data Pipeline 52