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

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Validity: Validity refers to whether the data accurately represents the concepts or phenomena it is intended to measure.

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. What you have to code is this workflow !

Architecture 130

Architecture Technology Google Cloud Metadata 130

François Nguyen

JANUARY 18, 2021

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. Every data governance policy about this topic must be read by a code to act in your data platform (access management, masking, etc.)

Data Governance 130

Data Governance Metadata Government Data Pipeline 130

Monte Carlo

MARCH 4, 2024

To do this, data observability solutions like Monte Carlo will piece together information from query logs, data aggregates and system metadata. All of this is to say that how table formats are structured, and specifically the metadata, impacts how they can be monitored and observed.

Metadata 52

Metadata Data Lake Data Warehouse Data Engineering 52

Cloudera

MARCH 9, 2021

In this blog, we’ll highlight the key CDP aspects that provide data governance and lineage and show how they can be extended to incorporate metadata for non-CDP systems from across the enterprise. The SDX layer of CDP leverages the full spectrum of Atlas to automatically track and control all data assets. Assets: Files.

Data Governance 93

Data Governance Government Metadata Datasets 93

Data Engineering Podcast

MAY 22, 2022

What is the workflow for someone building a new pipeline in Flyte? What are the patterns that you and the community have established to encourage discovery and reuse of granular task definitions? What are the available interfaces for extending Flyte and building integrations with other components across the data ecosystem?

Machine Learning 130

Machine Learning Data Engineering Data Engineer Cloud 130

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

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. You can observe your pipelines with built in metadata search and column level lineage.

Data Pipeline 100

Data Pipeline Data Metadata Software Engineer 100

Netflix Tech

JANUARY 25, 2023

Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. For example, we have a service that stores a movie entity’s metadata or a service that stores metadata about images. In this case it is BOUNDING_BOX.

Algorithm 113

Algorithm Media Metadata Data Ingestion 113

Monte Carlo

MARCH 12, 2024

With Dataplex, teams get lineage and visibility into their data management no matter where it’s housed, centralizing the security, governance, search and discovery across potentially distributed systems. Dataplex works with your metadata. Courtesy of Google Cloud.

Google Cloud 52

Google Cloud Metadata SQL Data Lake 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

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

Data Engineering Podcast

OCTOBER 28, 2019

In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and his vision for the future of data programming. If you are building dataflows then Dagster is definitely worth exploring.

Building 100

Building Data Pipeline Programming Language Metadata 100

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

Towards Data Science

APRIL 6, 2023

Alternatively, end-to-end tests, which assess a full system, stretching across repos and services, get overwhelmed by the cross-team complexity of dynamic data pipelines. Unit tests and end-to-end testing are necessary but insufficient to ensure high data quality in organizations with complex data needs and complex tables.

Unstructured Data 83

Unstructured Data Metadata Data Coding 83

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

MAY 16, 2022

Scale data lineage to meet the needs of the business Bottom line: if data lineage isn’t useful, it doesn’t matter What is data lineage? First, a quick refresher and data lineage definition. Making lineage useful by integrating metadata about all your data assets and pipelines in one cohesive view.

Metadata 52

Metadata Data Data Pipeline Big Data 52

Netflix Tech

DECEMBER 3, 2022

by Jasmine Omeke , Obi-Ike Nwoke , Olek Gorajek Intro This post is for all data practitioners, who are interested in learning about bootstrapping, standardization and automation of batch data pipelines at Netflix. You may remember Dataflow from the post we wrote last year titled Data pipeline asset management with Dataflow.

Data Pipeline 143

Data Pipeline Scala Metadata Food 143

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

Data Engineering Weekly

NOVEMBER 6, 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 data contract flow originated from the data producer.

Data Engineering 40

Data Engineering Data Engineer Engineering Machine Learning 40

Rockset

APRIL 18, 2023

In real-time ML, the freshness of the features, the serving latency, and the uptime and availability of the data pipeline and model matter. Let’s write some code that will generate interesting features by creating a feature definition file anomaly_detection_repo.py. You can read more on feature definition files here.

Machine Learning 52

Machine Learning Datasets Database Data Pipeline 52

Monte Carlo

SEPTEMBER 12, 2022

2) Open-source will increasingly infiltrate the data stack While open source isn’t a new trend, it’s definitely an accelerating one. Modeled after the principles of DevOps, data operations and data observability applies principles of software application observability and reliability to data.

BI 52

BI Data Governance ETL Tools Data Warehouse 52

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

Monte Carlo

JANUARY 25, 2023

That being said, it tends to be much easier to reprocess data in the data warehouse when we do find bad records, whereas that might not be possible in a streaming environment. Definition of data contracts Similar to contracts in production services, contracts in the warehouse should be implemented in code and version controlled.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 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

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

Monte Carlo

AUGUST 30, 2023

Your test passes when there are no rows returned, which indicates your data meets your defined conditions. Run the test again to validate that the initial problem is solved and that your data meets your quality and accuracy standards. This will help you debug any failures and understand the health of your data pipeline over time.

SQL 52

SQL Datasets Database Government 52

Knowledge Hut

APRIL 20, 2023

This may involve assigning data stewards and data custodians and implementing data governance committees. Data architecture and metadata management: It involves establishing a framework for data management, including data architecture and metadata management.

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

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

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Ripple Engineering

FEBRUARY 2, 2021

The Ripple Data Engineering team is expanding, which means higher frequency changes to our data pipeline source code. DAG Unit Testing Testing is underdeveloped in the data analytics world. A lot of big data warehousing projects have virtually no testing.

Building 52

Building Python Google Cloud Coding 52

Databand.ai

AUGUST 30, 2023

A shorter time-to-value indicates that your organization is efficient at processing and analyzing data for decision-making purposes. Monitoring this metric helps identify bottlenecks in the data pipeline and ensures timely insights are available for business users.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

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

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