Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

Data Lake 107

Data Lake Data Warehouse Cloud Unstructured Data 107

Uber Engineering

AUGUST 3, 2018

Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our products for riders, … The post Databook: Turning Big Data into Knowledge with Metadata at Uber appeared first on Uber Engineering Blog.

Metadata 110

Metadata Big Data Transportation Data 110

Netflix Tech

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Some of the optimizations are prerequisites for a high-performance data warehouse.

Data Warehouse 84

Data Warehouse Metadata Algorithm Data 84

Data Engineering Podcast

OCTOBER 14, 2019

Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. What are some of the challenges and mistakes that are common among engineers and analysts with regard to versioning and evolving schemas and the accompanying data?

Data Warehouse 100

Data Warehouse PostgreSQL AWS Programming Language 100

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

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. The results demonstrate superior price performance of Cloudera Data Warehouse on the full set of 99 queries from the TPC-DS benchmark. Introduction.

Data Warehouse 96

Data Warehouse Cloud Consulting SQL 96

Netflix Tech

OCTOBER 27, 2020

Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto , to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3.

Data Warehouse 100

Data Warehouse Datasets Data Big Data 100

Cloudera

MAY 17, 2021

Making a decision on a cloud data warehouse is a big deal. Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform.

Data Warehouse 90

Data Warehouse Cloud Government Metadata 90

Christophe Blefari

JUNE 21, 2024

Snowflake was founded in 2012 around its data warehouse product, which is still its core offering, and Databricks was founded in 2013 from academia with Spark co-creator researchers, becoming Apache Spark in 2014. It adds metadata, read, write and transactions that allow you to treat a Parquet file as a table.

Metadata 147

Metadata Data Warehouse BI MySQL 147

Data Engineering Podcast

FEBRUARY 5, 2023

Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses. Go to [dataengineeringpodcast.com/materialize]([link] Support Data Engineering Podcast

Data Engineering 147

Data Engineering Data Engineer Engineering PostgreSQL 147

Cloudera

APRIL 3, 2023

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Try Cloudera Data Warehouse (CDW) by signing up for a 60 day trial , or test drive CDP.

Data Warehouse 82

Data Warehouse Java Metadata Data 82

Snowflake

DECEMBER 4, 2024

The trend to centralize data will accelerate, making sure that data is high-quality, accurate and well managed. Overall, data must be easily accessible to AI systems, with clear metadata management and a focus on relevance and timeliness.

Unstructured Data 86

Unstructured Data Data Lake Deep Learning Structured Data 86

Engineering at Meta

FEBRUARY 4, 2025

Meta joins the Data Transfer Project and has continuously led the development of shared technologies that enable users to port their data from one platform to another. 2024: Users can access data logs in Download Your Information. What are data logs?

Accessible 80

Accessible Accessibility Raw Data Data Warehouse 80

Data Engineering Weekly

NOVEMBER 24, 2024

link] Jon Osborn: Best Practices for Using QUERY_TAG in Snowflake The modern data warehouses are good at running at scale, given the cost is not a constraint. The service offers configurable counter types optimized for various use cases with a unified Control Plane configuration. I’ve seen a similar work by Ben E.

Data Engineering 57

Data Engineering Data Engineer Engineering Insurance 57

Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 72

Data Warehouse Cloud Kafka Cloud Storage 72

Data Engineering Podcast

FEBRUARY 19, 2023

Summary Cloud data warehouses have unlocked a massive amount of innovation and investment in data applications, but they are still inherently limiting. Because of their complete ownership of your data they constrain the possibilities of what data you can store and how it can be used.

IT 147

IT Data Lake Metadata Data Warehouse 147

Data Engineering Podcast

AUGUST 13, 2022

Select Star’s data discovery platform solves that out of the box, with an automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day.

Metadata 100

Metadata MongoDB MySQL Scala 100

Data Engineering Weekly

MARCH 5, 2025

This ecosystem includes: Catalogs: Services that manage metadata about Iceberg tables (e.g., Compute Engines: Tools that query and process data stored in Iceberg tables (e.g., Maintenance Processes: Operations that optimize Iceberg tables, such as compacting small files and managing metadata. Trino, Spark, Snowflake, DuckDB).

Hadoop 57

Hadoop Metadata Data Ingestion Data Governance 57

phData: Data Engineering

NOVEMBER 8, 2024

First, we create an Iceberg table in Snowflake and then insert some data. Then, we add another column called HASHKEY , add more data, and locate the S3 file containing metadata for the iceberg table. In the screenshot below, we can see that the metadata file for the Iceberg table retains the snapshot history.

Architecture 52

Architecture Systems Data Lake Google Cloud 52

Maxime Beauchemin

JANUARY 20, 2017

Data modeling is changing Typical data modeling techniques — like the star schema  — which defined our approach to data modeling for the analytics workloads typically associated with data warehouses, are less relevant than they once were.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Data Engineering Podcast

DECEMBER 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. Missing data? Atlan is the metadata hub for your data ecosystem. Missing data? Stale dashboards?

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Data Engineering Podcast

DECEMBER 29, 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. Missing data? Atlan is the metadata hub for your data ecosystem. Missing data? Stale dashboards?

Management 130

Management Metadata Business Intelligence Data Lake 130

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. With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services. What you have to code is this workflow !

Technology 130

Technology Architecture Google Cloud Metadata 130

Monte Carlo

SEPTEMBER 10, 2024

This truth was hammered home recently when ride-hailing giant Uber found itself on the receiving end of a staggering €290 million ($324 million) fine from the Dutch Data Protection Authority. Poor data warehouse governance practices that led to the improper handling of sensitive European driver data. The reason?

Data Warehouse 52

Data Warehouse Government Data Governance Metadata 52

Sync Computing

NOVEMBER 7, 2024

Data volume and velocity, governance, structure, and regulatory requirements have all evolved and continue to. Despite these limitations, data warehouses, introduced in the late 1980s based on ideas developed even earlier, remain in widespread use today for certain business intelligence and data analysis applications.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Data Engineering Podcast

JUNE 19, 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.

Metadata 130

Metadata Unstructured Data MongoDB MySQL 130

Cloudera

DECEMBER 10, 2020

Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse. These costs impede the adoption of cloud-native data warehouses.

Data Warehouse 98

Data Warehouse Cloud Storage Metadata Data 98

Striim

MARCH 21, 2025

Since the value of data quickly drops over time, organizations need a way to analyze data as it is generated. To avoid disruptions to operational databases, companies typically replicate data to data warehouses for analysis.

IT 52

IT Data Lake Data Warehouse Relational Database 52

Netflix Tech

DECEMBER 3, 2022

The most commonly used one is dataflow project , which helps folks in managing their data pipeline repositories through creation, testing, deployment and few other activities. It lets you create YAML formatted mock data files based on selected tables, columns and a few rows of data from the Netflix data warehouse.

Data Pipeline 144

Data Pipeline Scala Metadata Food 144

Maxime Beauchemin

AUGUST 28, 2017

Consensus seeking Whether you think that old-school data warehousing concepts are fading or not, the quest to achieve conformed dimensions and conformed metrics is as relevant as it ever was. The data warehouse needs to reflect the business, and the business should have clarity on how it thinks about analytics.

Data Engineering 174

Data Engineering Data Engineer Engineering Software Engineer 174

Data Engineering Podcast

JUNE 26, 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.

Datasets 130

Datasets Unstructured Data Metadata MongoDB 130

Data Engineering Podcast

DECEMBER 19, 2021

Summary Building a well managed data ecosystem for your organization requires a holistic view of all of the producers, consumers, and processors of information. The team at Metaphor are building a fully connected metadata layer to provide both technical and social intelligence about your data. No more scripts, just SQL.

Systems 100

Systems Building Metadata Data Warehouse 100

Maxime Beauchemin

JANUARY 7, 2018

Note that where a TRUNCATE PARTITION is typically a “free” metadata operation, a DELETE operation may be expensive and that should be taken into considerations. This means that ideally the logic in source control describes how to build the full state of the data warehouse throughout all time periods.

Data Engineering 183

Data Engineering Data Engineer Data Process Process 183

Data Engineering Podcast

JUNE 17, 2021

In this episode he shares the goals of the Unstruk Data Warehouse, how it is architected to extract asset metadata and build a searchable knowledge graph from the information, and the myriad ways that the system can be used. Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Unstructured Data 100

Unstructured Data Data Warehouse Metadata Media 100

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 Podcast

NOVEMBER 20, 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 biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it.

Systems 130

Systems Metadata Data Pipeline MongoDB 130

Towards Data Science

DECEMBER 10, 2024

Take advantage of old school databasetricks In the last 1015 years weve seen massive changes to the data industry, notably big data, parallel processing, cloud computing, data warehouses, and new tools (lots and lots of newtools). Consequently, weve had to say goodbye to some things to make room for all this new stuff.

PostgreSQL 75

PostgreSQL Data Python SQL 75