Ascend.io

APRIL 22, 2024

This initiative is more than just an upgrade; it’s a reimagining of what a Data Automation Platform can be: dynamic, extensible, and highly intelligent. A unified platform that combines a powerful metadata core, an extensible plugin architecture, DataAware automation, and multiple AI Assistants.

Project 52

Project Metadata Data Pipeline Data Engineering 52

Data Engineering Podcast

NOVEMBER 6, 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 130

Building IT Metadata MongoDB 130

Towards Data Science

NOVEMBER 4, 2023

I’d like to discuss some popular Data engineering questions: Modern data engineering (DE). Does your DE work well enough to fuel advanced data pipelines and Business intelligence (BI)? Are your data pipelines efficient? What is it? So here are a few things to consider that can help us answer these questions.

Data Engineering 82

Data Engineering Data Engineer Engineering BI 82

Monte Carlo

JULY 11, 2023

Data observability solutions also take testing a step further by providing root cause analysis tools that help teams proactively identify the root cause of data inaccuracies, as they relate to code, your data infrastructure, and even the data itself.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

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. If you look at the BigQuery service (the cloud data warehouse in the Google Cloud Platform), you can start to write, transform and query your data without any provisioning.

Technology 130

Technology Architecture Google Cloud Metadata 130

Monte Carlo

AUGUST 7, 2023

Prevention: Finally, data observability also provides mechanisms to prevent data issues from happening in the first place, like placing circuit breaking in pipelines and creating visibility around the impact code changes would make on data, among other proactive measures of preventing bad data from entering your pipelines in the first place.

Coding 52

Coding Data Software Engineer Software Engineering 52

François Nguyen

JANUARY 18, 2021

This article was published in October 2020 with this title : “Data Governance as Code” The idea behind that is you should “actively promotes the safe use of data with automation that improves governance while freeing data analysts and scientists from manual tasks” The article is illustrated with many examples.

Data Governance 130

Data Governance Metadata Government Data Pipeline 130

Monte Carlo

MARCH 4, 2024

Table of Contents How Data Observability Works Made For The Lakehouse: Iceberg and Delta Transform in place: Dynamic Tables and Delta Live Tables Why This Matters How Data Observability Works Data observability provides full visibility into your data, systems, and code so you can find and fix bad data quickly.

Metadata 52

Metadata Data Lake Data Warehouse Data Engineering 52

Monte Carlo

AUGUST 30, 2023

More specifically, teams use dbt to write transformations as SQL queries, version control the code, and deploy transformations incrementally. dbt also happens to be an incredibly handy tool for running many data quality tests. Your test passes when there are no rows returned, which indicates your data meets your defined conditions.

SQL 52

SQL Datasets Database High Quality Data 52

Data Engineering Podcast

MAY 22, 2022

In this episode Ketan Umare and Haytham Abuelfutuh share the story of the Flyte project and how their work at Union is focused on supporting and scaling the code and community that has made Flyte successful. What is the workflow for someone building a new pipeline in Flyte?

Machine Learning 130

Machine Learning Data Engineering Data Engineer Cloud 130

Snowflake

DECEMBER 12, 2023

To give customers flexibility for how they fit Snowflake into their data architecture, Iceberg Tables can be configured to use either Snowflake or an external service such as AWS Glue as the table’s catalog to track metadata, with an easy, one-line SQL command to convert the table’s catalog to Snowflake in a metadata-only operation.

Metadata 90

Metadata Python AWS Government 90

Ascend.io

FEBRUARY 28, 2024

Engineers ensure the availability of clean, structured data, a necessity for AI systems to learn from patterns, make accurate predictions, and automate decision-making processes. Through the design and maintenance of efficient data pipelines , data engineers facilitate the seamless flow and accessibility of data for AI processing.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Data Engineering Podcast

JANUARY 7, 2022

Summary Data observability is a set of technical and organizational capabilities related to understanding how your data is being processed and used so that you can proactively identify and fix errors in your workflows. Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud.

BI 100

BI Data Warehouse Metadata SQL 100

Data Engineering Podcast

MAY 18, 2021

Summary Data lineage is the common thread that ties together all of your data pipelines, workflows, and systems. In order to get a holistic understanding of your data quality, where errors are occurring, or how a report was constructed you need to track the lineage of the data from beginning to end.

Metadata 100

Metadata Kafka Data Warehouse Hadoop 100

Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. Those who don’t embrace it will be left behind. John agrees. “

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

Monte Carlo

MARCH 28, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Zero-ETL What it is : A misnomer for one thing; the data pipeline still exists.

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Data Engineering Podcast

FEBRUARY 6, 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. The only thing worse than having bad data is not knowing that you have it.

Data Pipeline 100

Data Pipeline Data Metadata Software Engineer 100

RandomTrees

FEBRUARY 6, 2024

Transformation: Shaping Data for the Future: LLMs facilitate standardizing date formats with precision and translation of complex organizational structures into logical database designs, streamline the definition of business rules, automate data cleansing, and propose the inclusion of external data for a more complete analytical view.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Netflix Tech

JANUARY 10, 2024

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. By integrating with studio content systems, we enabled the pipeline to leverage rich metadata from the creative side and create more engaging member experiences like interactive storytelling. This testing stage took about two weeks.

Process 91

Process Pipeline-centric Media Metadata 91

Engineering at Meta

OCTOBER 31, 2023

Meta’s Systematic Code and Asset Removal Framework (SCARF) has a subsystem for identifying and removing unused data types. SCARF scans production data systems to identify tables or assets that are unused and safely removes them. Each represents a class of data — not individual records.

Data 93

Data Metadata Coding Relational Database 93

Monte Carlo

JANUARY 23, 2024

The speed of ETL and analytics has increased Maxime recalls a time, not too long ago, when data engineering would require running Hive jobs for hours at a time, frequent context switching between jobs and managing different elements of your data pipeline. The data warehouse is the mirror of the organization in many ways.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

Monte Carlo

FEBRUARY 9, 2023

Pie Insurance , a leading small business insurtech, leverages a data vault 2.0 architecture (with some minor deviations) to achieve their data integration objectives around scalability and use of metadata. “A How do you implement a data vault architecture? This layer has minimal transformation rules.

Architecture 52

Architecture Raw Data Metadata Data Warehouse 52

Data Engineering Podcast

MAY 3, 2021

They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” In DataOps, the definition of done includes more than just some working code. Figure 1 shows a manually executed data analytics pipeline.

Engineering 60

Engineering BI Business Analyst Metadata 60

DataKitchen

AUGUST 8, 2023

In the article, Melody Chien notes that Data Observability is a practice that extends beyond traditional monitoring and detection, providing robust, integrated visibility over data and data landscapes. It alerts data and analytics leaders to issues with their data before they multiply.

Data Pipeline 52

Data Pipeline Data Government Metadata 52

Towards Data Science

APRIL 3, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Zero-ETL What it is : A misnomer for one thing; the data pipeline still exists.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

Monte Carlo

AUGUST 10, 2023

What is Data observability? The five pillars My data observability definition has not changed since I first coined it in 2019: Data observability refers to an organization’s comprehensive understanding of the health and performance of the data within their systems.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Data Engineering Podcast

NOVEMBER 20, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. jobs, ops, graphs) One of the powerful elements of the Dagster framework is the investment in rich metadata as a foundational principle.

Data Warehouse 100

Data Warehouse Data Lake BI Business Intelligence 100

Towards Data Science

APRIL 6, 2023

How to know if you need data observability Data observability is increasingly necessary, especially as traditional approaches to software monitoring fall short for high-volume, high-variety data. That’s possibly correct — but the size of the distributional shift should definitely lead to an anomaly alert!

Unstructured Data 79

Unstructured Data Metadata Data Coding 79

Data Engineering Podcast

NOVEMBER 9, 2020

In this episode Grant Seward explains how he built Tree Schema to be an easy to use and cost effective option for organizations to build their data catalogs. He also shares the internal architecture, how he approached the design to make it accessible and easy to use, and how it autodiscovers the schemas and metadata for your source systems.

Building 100

Building PostgreSQL BI Metadata 100

Monte Carlo

FEBRUARY 8, 2024

GigaOm GigaOm’s Data Observability Radar Report covers the problem data observability tools look to solve saying, “Data observability is critical for countering, if not eliminating, data downtime, in which the results of analytics or the performance of applications are compromised because of unhealthy, inaccurate data.”

BI 59

BI Data Pipeline Data Warehouse Data 59

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Netflix Tech

OCTOBER 18, 2022

Usability Netflix is a data-driven company, where key decisions are driven by data insights, from the pixel color used on the landing page to the renewal of a TV-series. Data scientists, engineers, non-engineers, and even content producers all run their data pipelines to get the necessary insights.

Java 81

Java Data Machine Learning Systems 81

François Nguyen

MARCH 7, 2021

In this article, Juan Sequada gives maybe one of the best definition of Data Mesh ” It is paradigm shift towards a distributed architecture that attempts to find an ideal balance between centralization and decentralization of metadata and data management.”

Government 130

Government Data Governance Data Metadata 130

ProjectPro

JANUARY 24, 2023

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. Furthermore, AWS Glue DataBrew allows you to visually clean and normalize data without any code.

AWS 52

AWS Data Lake ETL Tools Scala 52

Data Engineering Podcast

OCTOBER 14, 2019

Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. They provide an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure.

Data Warehouse 100

Data Warehouse PostgreSQL AWS Programming Language 100

Confluent

FEBRUARY 22, 2019

The requirements for fast and reliable data pipelines are growing quickly at Deliveroo as the business continues to grow and innovate. We have delivered an event streaming platform which gives strong guarantees on data quality, using Apache Kafka ® and Protocol Buffers. Protobuf Rules That We Decided to Enforce.

Kafka 79

Kafka Programming Language Metadata Data 79