Christophe Blefari

MARCH 15, 2024

Saying mainly that " Sora is a tool to extend creativity " Last point Mira has been mocked and criticised online because as a CTO she wasn't able to say on which public / licensed data Sora has been trained on. This is related to Paris testing automated video surveillance during Olympics. This is Croissant.

Metadata 272

Metadata Software Engineering Data Warehouse Software Engineer 272

ProjectPro

JUNE 6, 2025

Building a batch pipeline is essential for processing large volumes of data efficiently and reliably. Are you ready to step into the heart of big data projects and take control of data like a pro? Are you ready to step into the heart of big data projects and take control of data like a pro?

Data Pipeline 40

Data Pipeline Building Retail Data Ingestion 40

François Nguyen

FEBRUARY 28, 2021

Today, we are going to apply these principles to the data pipelines. The idea is to transpose these 7 principles to data pipeline knowing that Data pipelines are 100% flexible : if you have the skills, you build the pipeline you want. How does a bad data pipeline process look like ?

Data Pipeline 130

Data Pipeline Building Manufacturing BI 130

LinkedIn Engineering

JUNE 26, 2023

However, we've found that this vertical self-service model doesn't work particularly well for data pipelines, which involve wiring together many different systems into end-to-end data flows. Data pipelines power foundational parts of LinkedIn's infrastructure, including replication between data centers.

Data Pipeline 112

Data Pipeline Kafka MySQL SQL 112

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 105

Metadata Data Pipeline Government Data 105

Engineering at Meta

APRIL 28, 2025

Managing and understanding large-scale data ecosystems is a significant challenge for many organizations, requiring innovative solutions to efficiently safeguard user data. To address these challenges, we made substantial investments in advanced data understanding technologies, as part of our Privacy Aware Infrastructure (PAI).

Metadata 83

Metadata Data Utilities Data Warehouse 83

ProjectPro

JUNE 6, 2025

Want to process peta-byte scale data with real-time streaming ingestions rates, build 10 times faster data pipelines with 99.999% reliability, witness 20 x improvement in query performance compared to traditional data lakes, enter the world of Databricks Delta Lake now. Delta Lake is a game-changer for big data.

Data Lake 63

Data Lake Data Warehouse Metadata Unstructured Data 63

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 Manufacturing Building 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 Engineer Data Engineering 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

ProjectPro

JUNE 6, 2025

Discover 50+ Azure Data Factory interview questions and answers for all experience levels. A report by ResearchAndMarkets projects the global data integration market size to grow from USD 12.24 A report by ResearchAndMarkets projects the global data integration market size to grow from USD 12.24 billion in 2020 to USD 24.84

Data Lake 60

Data Lake Metadata SQL Datasets 60

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 Engineer 57

Data Engineer Data Engineering 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

ProjectPro

JUNE 6, 2025

Data engineering is the foundation for data science and analytics by integrating in-depth knowledge of data technology, reliable data governance and security, and a solid grasp of data processing. Data engineers need to meet various requirements to build data pipelines.

AWS 52

AWS Data Engineer Data Engineering Project 52

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

Teradata

MAY 30, 2025

Register now Home Insights Data platform Article How To Use Airbyte, dbt-teradata, Dagster, and Teradata Vantage™ for Seamless Data Integration Build and orchestrate a data pipeline in Teradata Vantage using Airbyte, Dagster, and dbt. In this case, we select Sample Data (Faker). dbt-core dagster==1.7.9

Data Integration 52

Data Integration Raw Data Metadata Data Pipeline 52

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

ProjectPro

JUNE 6, 2025

What is a Machine Learning Pipeline? A machine learning pipeline helps automate machine learning workflows by processing and integrating data sets into a model, which can then be evaluated and delivered. A well-built pipeline helps in the flexibility of the model implementation.

Machine Learning 58

Machine Learning Building Amazon Web Services Deep Learning 58

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.

Accessible 89

Accessible Accessibility Raw Data Data Warehouse 89

Cloudera

NOVEMBER 15, 2024

In fact, according to the Identity Theft Resource Center (ITRC) Annual Data Breach Report , there were 2,365 cyber attacks in 2023 with more than 300 million victims, and a 72% increase in data breaches since 2021. However, there is a fundamental challenge standing in the way of being successful: data.

Metadata 84

Metadata Unstructured Data Data Lake Government 84

ProjectPro

JUNE 6, 2025

Due to emerging cloud technologies, many companies are increasingly migrating their data using ETL workflows. Most of them use old, inflexible, and vulnerable RDBMS or other types of data storage. ETL processes are useful for moving many data sources to a single data warehousing location.

ETL Tools 40

ETL Tools AWS Data Engineer Data Engineering 40

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.

Data Pipeline 98

Data Pipeline AWS Amazon Web Services Python 98

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

Data Engineering Weekly

JUNE 1, 2025

Join Dagster and Neurospace to learn: - How to build AI pipelines with orchestration baked in - How to track data lineage for audits and traceability - Tips for designing compliant workflows under the EU AI Act Register for the technical session DuckDB: DuckLake - SQL as a Lakehouse Format DuckDB announced a new open table format, DuckLake.

Data Engineer 70

Data Engineer Data Engineering Engineering Relational Database 70

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

Precisely

JANUARY 6, 2025

Key takeaways : Real-time data is critical for exceptional customer experiences. Customers expect immediate responses and personalized interactions, and streaming data architectures help you meet these expectations. Real-time data unlocks actionable insights and competitive advantage. How do you meet these high expectations?

Data Architecture 59

Data Architecture Architecture Pipeline-centric Banking 59

ProjectPro

JUNE 6, 2025

The adaptability and technical superiority of such open-source big data projects make them stand out for community use. As per the surveyors, Big data (35 percent), Cloud computing (39 percent), operating systems (33 percent), and the Internet of Things (31 percent) are all expected to be impacted by open source shortly.

Big Data 49

Big Data Project Metadata Programming Language 49

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.

IT 130

IT Building 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

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

ProjectPro

JUNE 6, 2025

Looking for an efficient tool for streamlining and automating your data processing workflows? Let's consider an example of a data processing pipeline that involves ingesting data from various sources, cleaning it, and then performing analysis. Apache Airflow DAGs are your one-stop solution!

Data Pipeline 63

Data Pipeline PostgreSQL Python Database 63

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 MongoDB Data Pipeline 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 Scala MySQL 100

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

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 Data Lake Business Intelligence MongoDB 130