Snowflake

JANUARY 25, 2023

Data, data, data. It does seem we are not only surrounded by talk about data, but by the actual data itself. We are collecting data from every nook and cranny of the universe (literally!). We cannot scale our expertise as fast as we can scale the Data Cloud. This must change. The solution?

Metadata 102

Metadata Data Pipeline Government Data 102

Data Engineering Podcast

NOVEMBER 10, 2021

Summary A significant source of friction and wasted effort in building and integrating data management systems is the fragmentation of metadata across various tools. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform.

Metadata 100

Metadata Data Warehouse Data Lake BI 100

Cloudera

FEBRUARY 8, 2021

You can read part 1, here: Digital Transformation is a Data Journey From Edge to Insight. The first blog introduced a mock connected vehicle manufacturing company, The Electric Car Company (ECC), to illustrate the manufacturing data path through the data lifecycle. 1 The enterprise data lifecycle.

Data Pipeline 118

Data Pipeline Building Manufacturing Data Warehouse 118

Databand.ai

JUNE 28, 2023

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Data Engineering Podcast

OCTOBER 15, 2021

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. No more scripts, just SQL.

Metadata 100

Metadata BI Data Warehouse Government 100

Data Engineering Weekly

NOVEMBER 24, 2024

Editor’s Note: Launching Data & Gen-AI courses in 2025 I can’t believe DEW will reach almost its 200th edition soon. What I started as a fun hobby has become one of the top-rated newsletters in the data engineering industry. We are planning many exciting product lines to trial and launch in 2025.

Data Engineering 57

Data Engineering Data Engineer Engineering Insurance 57

Ascend.io

DECEMBER 4, 2024

As we approach 2025, data teams find themselves at a pivotal juncture. The rapid evolution of technology and the increasing demand for data-driven insights have placed immense pressure on these teams. The future of data teams depends on their ability to adapt to new challenges and seize emerging opportunities.

Data Pipeline 52

Data Pipeline Metadata Data Workflow Data 52

Monte Carlo

APRIL 28, 2025

Different teams love using the same data in totally different ways. Thats where data dictionary tools come in. A data dictionary tool helps define and organize your data so everyones speaking the same language. A data dictionary tool helps define and organize your data so everyones speaking the same language.

Metadata 64

Metadata Hadoop Data SQL 64

Cloudera

AUGUST 6, 2024

Over the past several years, data leaders asked many questions about where they should keep their data and what architecture they should implement to serve an incredible breadth of analytic use cases. The future for most data teams will be multi-cloud and hybrid. It no longer matters where the data is.

Metadata 82

Metadata Government Datasets Architecture 82

Data Engineering Podcast

JUNE 16, 2024

Summary Stripe is a company that relies on data to power their products and business. In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform.

Data Lake 147

Data Lake High Quality Data Metadata Machine Learning 147

Engineering at Meta

FEBRUARY 4, 2025

Were sharing how Meta built support for data logs, which provide people with additional data about how they use our products. Here we explore initial system designs we considered, an overview of the current architecture, and some important principles Meta takes into account in making data accessible and easy to understand.

Accessibility 93

Accessibility Accessible Raw Data Data Warehouse 93

Data Engineering Podcast

NOVEMBER 13, 2022

Summary Building data products is an undertaking that has historically required substantial investments of time and talent. With the rise in cloud platforms and self-serve data technologies the barrier of entry is dropping. Atlan is the metadata hub for your data ecosystem.

Building 130

Building Metadata MongoDB MySQL 130

Ascend.io

DECEMBER 4, 2024

As we approach 2025, data teams find themselves at a pivotal juncture. The rapid evolution of technology and the increasing demand for data-driven insights have placed immense pressure on these teams. The future of data teams depends on their ability to adapt to new challenges and seize emerging opportunities.

Data Pipeline 52

Data Pipeline Metadata Data Workflow Data 52

Towards Data Science

APRIL 6, 2023

Today’s post follows the same philosophy: fitting local and cloud pieces together to build a data pipeline. And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them). not sponsored.

AWS 98

AWS Data Pipeline Amazon Web Services Python 98

Data Engineering Podcast

NOVEMBER 6, 2022

Summary A lot of the work that goes into data engineering is trying to make sense of the "data exhaust" from other applications and services. Atlan is the metadata hub for your data ecosystem. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day.

Building 130

Building IT Metadata MongoDB 130

Jesse Anderson

NOVEMBER 14, 2023

A Guest Post by Ole Olesen-Bagneux In this blog post I would like to describe a new data team, that I call ‘the data discovery team’. Data discovery is thought of in different ways in data science and in information science respectfully. In an enterprise data reality, searching for data is a bit of a hassle.

Metadata 147

Metadata Data Science Big Data Data 147

Data Engineering Podcast

NOVEMBER 20, 2022

Summary The majority of blog posts and presentations about data engineering and analytics assume that the consumers of those efforts are internal business users accessing an environment controlled by the business. Atlan is the metadata hub for your data ecosystem.

Systems 130

Systems Metadata Data Pipeline MongoDB 130

Towards Data Science

OCTOBER 25, 2023

Not too long ago, almost all data architectures and data team structures followed a centralized approach. As a data or analytics engineer, you knew where to find all the transformation logic and models because they were all in the same codebase. There was only one data team, two at most.

Data Pipeline 98

Data Pipeline SQL Data Architecture Data 98

Data Engineering Podcast

DECEMBER 18, 2022

Summary One of the reasons that data work is so challenging is because no single person or team owns the entire process. This introduces friction in the process of collecting, processing, and using data. In order to reduce the potential for broken pipelines some teams have started to adopt the idea of data contracts.

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Netflix Tech

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. We expect complete and accurate data at the end of each run.

Data Engineering 110

Data Engineering Data Engineer Engineering Metadata 110

Cloudera

MARCH 14, 2023

We just announced the general availability of Cloudera DataFlow Designer , bringing self-service data flow development to all CDP Public Cloud customers. In this blog post we will put these capabilities in context and dive deeper into how the built-in, end-to-end data flow life cycle enables self-service data pipeline development.

Data Pipeline 84

Data Pipeline Designing Kafka Metadata 84

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. 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 ?

Technology 130

Technology Architecture Google Cloud Metadata 130

Data Engineering Podcast

AUGUST 13, 2022

Summary Data is useless if it isn’t being used, and you can’t use it if you don’t know where it is. Data catalogs were the first solution to this problem, but they are only helpful if you know what you are looking for. Data stacks are becoming more and more complex. Sifflet also offers a 2-week free trial.

Metadata 100

Metadata MongoDB MySQL Scala 100

Data Engineering Weekly

NOVEMBER 3, 2024

The challenges around memory, data size, and runtime are exciting to read. Sampling is an obvious strategy for data size, but the layered approach and dynamic inclusion of dependencies are some key techniques I learned with the case study. This count helps to ensure data consistency when deleting and compacting segments.

Data Engineering 62

Data Engineering Data Engineer Engineering Pipeline-centric 62

Edureka

MAY 27, 2025

Microsoft Fabric is a next-generation data platform that combines business intelligence, data warehousing, real-time analytics, and data engineering into a single integrated SaaS framework. The architecture of Microsoft Fabric is based on several essential elements that work together to simplify data processes: 1.

Architecture 52

Architecture BI Business Intelligence Raw Data 52

Data Engineering Podcast

OCTOBER 23, 2022

Applying those same practices to data can prove challenging due to the number of systems that need to be included to implement a complete feature. Atlan is the metadata hub for your data ecosystem. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day.

Project 130

Project Metadata MongoDB MySQL 130

Data Engineering Podcast

JULY 17, 2022

Summary There are extensive and valuable data sets that are available outside the bounds of your organization. Whether that data is public, paid, or scraped it requires investment and upkeep to acquire and integrate it with your systems. Atlan is the metadata hub for your data ecosystem.

Data Management 130

Data Management Management Metadata MongoDB 130

Data Engineering Weekly

FEBRUARY 23, 2025

Try Astro Free → Editor’s Note: Data Council 2025, Apr 22-24, Oakland, CA Data Council has always been one of my favorite events to connect with and learn from the data engineering community. Data Council 2025 is set for April 22-24 in Oakland, CA. The results? will shape the future of DataOps.

Data Engineering 62

Data Engineering Data Engineer Engineering Kafka 62

Data Engineering Podcast

JUNE 19, 2022

Summary Data analysis is a valuable exercise that is often out of reach of non-technical users as a result of the complexity of data systems. Atlan is the metadata hub for your data ecosystem. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Metadata 130

Metadata Unstructured Data MongoDB MySQL 130

Netflix Tech

DECEMBER 14, 2023

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community!

Data Engineering 107

Data Engineering Data Engineer Engineering Metadata 107

Data Engineering Weekly

MARCH 23, 2025

Editor’s Note: Data Council 2025, Apr 22-24, Oakland, CA Data Council has always been one of my favorite events to connect with and learn from the data engineering community. Data Council 2025 is set for April 22-24 in Oakland, CA. These are common LinkedIn requests. The article resonated with me when I read it.

Data Engineering 62

Data Engineering Data Engineer Engineering Data 62

Data Engineering Podcast

OCTOBER 30, 2022

In its current incarnation it has become a ubiquitous need for analytics and opportunities to answer questions with data. In this episode Amir Orad discusses the Sisense platform and how it facilitates the embedding of analytics and data insights in every aspect of organizational and end-user experiences.

Business Intelligence 130

Business Intelligence Metadata MongoDB MySQL 130

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. In every step,we do not just read, transform and write data, we are also doing that with the metadata. Last part, it was added the data security and privacy part.

Data Governance 130

Data Governance Metadata Government Data Pipeline 130

Data Engineering Weekly

APRIL 20, 2025

[link] Jing Ge: Context Matters — The Vision of Data Analytics and Data Science Leveraging MCP and A2A All aspects of software engineering are rapidly being automated with various coding AI tools, as seen in the AI technology radar. Data engineering is one aspect where I see a few startups starting to disrupt.

Data Engineering 67

Data Engineering Data Engineer Engineering Kafka 67

Data Engineering Weekly

JUNE 16, 2024

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. Regulative requirements and privacy concerns are often a big hurdle to training context-rich data.

Data Engineering 104

Data Engineering Data Engineer Engineering Kafka 104

Data Engineering Podcast

DECEMBER 29, 2022

Summary Making effective use of data requires proper context around the information that is being used. Rehgan Avon co-founded AlignAI to help address this challenge through a more purposeful platform designed to collect and distribute the knowledge of how and why data is used in a business. Struggling with broken pipelines?

Management 130

Management Metadata Business Intelligence Data Lake 130

Snowflake

MAY 15, 2024

According to Infosys, 35% of AI projects will either fail or experience delays because of poor data quality. There’s a huge gap between the data quality most companies have by default and the data quality needed for successful AI. Metaplane ensures that every company can trust the data that powers their business.

Cloud 126

Cloud Metadata Data Pipeline Government 126