Ascend.io

JULY 11, 2024

Metadata is the information that provides context and meaning to data, ensuring it’s easily discoverable, organized, and actionable. It enhances data quality, governance, and automation, transforming raw data into valuable insights. This is what managing data without metadata feels like. Chaos, right?

Metadata 52

Metadata IT Government High Quality Data 52

Data Engineering Podcast

JULY 17, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Data Management 130

Data Management Management Metadata MongoDB 130

Data Engineering Weekly

NOVEMBER 3, 2024

Data Engineering Weekly readers get 15% discount by registering the following link, [link] Gustavo Akashi: Building data pipelines effortlessly with a DAG Builder for Apache Airflow Every code-first data workflow grew into a UI-based or Yaml-based workflow.

Data Engineering 62

Data Engineering Data Engineer Engineering Pipeline-centric 62

Netflix Tech

NOVEMBER 14, 2023

Now, let’s explore the state of our pipelines after incorporating Psyberg. Pipelines After Psyberg Let’s explore how different modes of Psyberg could help with a multistep data pipeline. The session metadata table can then be read to determine the pipeline input.

Metadata 101

Metadata Data Pipeline Scala Data Workflow 101

Engineering at Meta

FEBRUARY 4, 2025

For each data logs table, we initiate a new worker task that fetches the relevant metadata describing how to correctly query the data. Once we know what to query for a specific table, we create a task for each partition that executes a job in Dataswarm (our data pipeline system).

Accessible 81

Accessible Accessibility Raw Data Data Warehouse 81

Data Engineering Podcast

AUGUST 28, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

Data Engineering Podcast

SEPTEMBER 18, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc.,

Building 100

Building Metadata MongoDB MySQL 100

Data Engineering Podcast

NOVEMBER 26, 2023

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Architecture 147

Architecture Data Lake High Quality Data SQL 147

Data Engineering Podcast

JULY 31, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. The only thing worse than having bad data is not knowing that you have it. Atlan is the metadata hub for your data ecosystem.

IT 100

IT Metadata MongoDB MySQL 100

Data Engineering Podcast

FEBRUARY 20, 2022

Summary The life sciences as an industry has seen incredible growth in scale and sophistication, along with the advances in data technology that make it possible to analyze massive amounts of genomic information. RudderStack’s smart customer data pipeline is warehouse-first.

Systems 100

Systems Software Engineer Software Engineering Data Warehouse 100

Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Data Engineering Podcast

JANUARY 30, 2022

Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. You can observe your pipelines with built in metadata search and column level lineage.

Data Engineering 100

Data Engineering Data Engineer Engineering Python 100

Data Engineering Podcast

APRIL 5, 2021

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. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Podcast

MAY 3, 2021

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Netflix Tech

NOVEMBER 20, 2023

The need for backfilling could be due to a variety of factors, e.g. (1) upstream data sets got repopulated due to changes in business logic of its data pipeline, (2) business logic was changed in a data pipeline, (3) anew metric was created that needs to be populated for historical time ranges, (4) historical data was found missing, etc.

Process 87

Process Data Pipeline Datasets SQL 87

Workfall

JUNE 12, 2023

It facilitates data synchronisation, replication, real-time analytics, and event-driven processing, empowering data-driven decision-making and operational efficiency. These additional columns store metadata like timestamps, user IDs, and change types, ensuring granular change tracking and auditability.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Data Engineering Weekly

DECEMBER 16, 2024

Grab’s Metasense , Uber’s DataK9 , and Meta’s classification systems use AI to automatically categorize vast data sets, reducing manual efforts and improving accuracy. Beyond classification, organizations now use AI for automated metadata generation and data lineage tracking, creating more intelligent data infrastructures.

Data Engineering 62

Data Engineering Data Engineer Engineering Unstructured Data 62

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. Data Integration Data integration is the process of collecting, transforming, and consolidating data from various sources.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Monte Carlo

MAY 14, 2024

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. Table formats incorporate aspects like columns, rows, data types, and relationships, but can also include information about the structure of the data itself.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Ascend.io

DECEMBER 11, 2023

Here’s the deal: for data to truly drive your business forward, you need a reliable and scalable system to keep it moving without hiccups. In other words, you need data orchestration. In this article, we’ll break down what data orchestration is, its significance, and how it differs from data pipeline orchestration.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

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

Data Engineering Weekly

OCTOBER 30, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. The highlights are that 59% of folks think data catalogs are sometimes helpful.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Data Engineering Weekly

JANUARY 15, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. 2023 predictions from the panel are; Unified metadata becomes kingmaker.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Netflix Tech

OCTOBER 18, 2022

With the high growth of workflows in the past few years?—?increasing increasing at > 100% a year, the need for a scalable data workflow orchestrator has become paramount for Netflix’s business needs. A workflow instance is an execution of a workflow, similarly, an execution of a step is called a step instance.

Java 84

Java Data Machine Learning Systems 84

Ascend.io

MAY 19, 2023

At this stage of the innovation cycle, engineers are in dire need of a solution that will give them broader control of their data pipelines and automate the manual activities holding them back. That’s where the post-modern data stack — stage 4 of the innovation cycle — comes into play. What is the post-modern data stack?

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Edureka

JULY 3, 2024

ADF connects to various data sources, including on-premises systems, cloud services, and SaaS applications. It then gathers and relocates information to a centralized hub in the cloud using the Copy Activity within data pipelines. Transform and Enhance the Data: Once centralized, data undergoes transformation and enrichment.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. Technical Challenges Choosing appropriate tools and technologies is critical for streamlining data workflows across the organization.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Monte Carlo

AUGUST 13, 2024

Here’s how Gartner officially defines the category of data observability tools: “Data observability tools are software applications that enable organizations to understand the state and health of their data, data pipelines, data landscapes, data infrastructures, and the financial operational cost of the data across distributed environments.

Data 52

Data Data Warehouse Data Pipeline Data Architecture 52

Monte Carlo

SEPTEMBER 20, 2022

Here’s how Prefect , Series B startup and creator of the popular data orchestration tool, harnessed the power of data observability to preserve headcount, improve data quality and reduce time to detection and resolution for data incidents. But a growing company means growing data needs. Image courtesy of Prefect.

Big Data 52

Big Data Data Warehouse Data Data Governance 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

AltexSoft

MARCH 14, 2023

Also, Databricks are pioneering the lakehouse concept that makes it possible to use data management features inherent in data warehousing on the raw data stored in a low-cost data lake owing to its metadata layer. Data orchestration involves managing the scheduling and execution of data workflows.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Ascend.io

MARCH 19, 2025

The best engineering teams arent just optimizing pipelines; theyre designing resilient and scalable data architectures that minimize downtime, accelerate deployment, and enhance data trust. Below, we break down five essential best practices to help your data pipelines remain efficient, secure, and adaptable in 2025.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

AltexSoft

NOVEMBER 7, 2022

The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. It still remains a leading workflow management tool adopted by thousands of companies, from tech giants to startups. How data engineering works. Metadata database.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

Data Engineering Weekly

DECEMBER 18, 2024

Imagine an IDE that not only helps you write code but also understands the semantics of your data and can suggest the best way to transform it. It will break down the barriers between technical and non-technical teams, accelerating data-driven innovation. Expect greater adoption of standards like Delta Sharing and continued evolution.

Data Engineering 113

Data Engineering Data Engineer Engineering Data Lake 113