Data Engineering Podcast

JUNE 12, 2022

Summary Building a well rounded and effective data team is an iterative process, and the first hire can set the stage for future success or failure. Trupti Natu has been the first data hire multiple times and gone through the process of building teams across the different stages of growth.

Metadata 100

Metadata Unstructured Data Business Intelligence MongoDB 100

Data Engineering Podcast

NOVEMBER 20, 2022

In this episode CEO and founder Salma Bakouk shares her views on the causes and impacts of "data entropy" and how you can tame it before it leads to failures. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.

Data Lake 130

Data Lake Data Ingestion MongoDB Scala 130

Christophe Blefari

JUNE 16, 2023

Why data consumers do not trust your reporting — It is a good illustration of the data journey manifesto. Stakeholders often notice data issues before the data team does. Data warehouses are mutable, this is one of the many root causes proposed by Lucas. Data Documentation 101: Why?

Programming Language 130

Programming Language SQL PostgreSQL Data 130

Data Engineering Podcast

MAY 18, 2021

In order to eliminate the wasted effort of building custom integrations every time you want to combine lineage information across systems Julien Le Dem introduced the OpenLineage specification. RudderStack’s smart customer data pipeline is warehouse-first. What are your goals for the OpenLineage effort?

Metadata 100

Metadata Kafka Data Warehouse Hadoop 100

Data Engineering Podcast

JUNE 5, 2022

Summary The best way to make sure that you don’t leak sensitive data is to never have it in the first place. The team at Skyflow decided that the second best way is to build a storage system dedicated to securely managing your sensitive information and making it easy to integrate with your applications and data systems.

Data Security 100

Data Security Metadata MongoDB Scala 100

Data Engineering Podcast

DECEMBER 16, 2019

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Data Engineering Weekly

JULY 9, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork, and SQL grunt work out of building complete customer profiles, so you can quickly ship actionable, enriched data to every downstream team. Find the right, trusted data by simply asking a question.

Data Engineering 52

Data Engineering Data Engineer Engineering Lambda Architecture 52

Cloudera

MARCH 17, 2023

I love the enthusiasm surrounding ChatGPT and the eagerness to learn about modern data architectures such as data lakehouses, data meshes, and data fabrics. ChatGPT is an excellent resource for gaining high-level insights and building awareness of any technology.

Education 62

Education Unstructured Data Data Lake Data Warehouse 62

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

Data Engineering Podcast

SEPTEMBER 23, 2018

This includes modeling the lifecycle of your information as a pipeline from the raw, messy, loosely structured records in your data lake, through a series of transformations and ultimately to your data warehouse. What is your opinion on the relative merits of a data warehouse vs a data lake and are they mutually exclusive?

Data Lake 100

Data Lake Data Warehouse Data Architecture Architecture 100

Monte Carlo

APRIL 24, 2023

Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. Databricks Data Catalog and AWS Lake Formation are examples in this vein. See our post: Data Lakes vs. Data Warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Ascend.io

APRIL 7, 2023

Data mesh’s distributed nature helps data engineering teams become more agile , connecting data product building blocks that already exist to form other data products. In other words, the product one person builds might have inputs from another product. They can build on each other’s work.

Metadata 52

Metadata Datasets Data Governance Government 52

Data Engineering Weekly

JULY 2, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork, and SQL grunt work out of building complete customer profiles, so you can quickly ship actionable, enriched data to every downstream team. Now every team can build a customer360 in Snowflake with RudderStack Profiles.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Snowflake

FEBRUARY 29, 2024

And because these tiles often need to perfectly connect with each other to build out or populate the map, the tiles often come in one of several shapes: a square, triangle or hexagon. This grid, along with the metadata that represents the details of how the grid is constructed and used, is referred to as a spatial index.

Insurance 86

Insurance SQL BI Consulting 86

Snowflake

APRIL 17, 2023

What started as a cloud data warehouse evolved into the Data Cloud, which includes the content—data, services, and applications—that customers and partners bring to the cloud. But we’re also seeing more interest in acquiring external data and finding ways to share data with others.

Retail 69

Retail Metadata Cloud Data Warehouse 69

dbt Developer Hub

JANUARY 24, 2023

Ensures developers can work quickly by shortening the CI feedback loop Reduce costs in your data warehouse by running only what's been modified I think we can all agree that increasing developer productivity while simultaneously reducing costs is an outcome that every company strives for. A query method on the metadata property.

Metadata 52

Metadata Python Cloud Data Warehouse 52

Netflix Tech

OCTOBER 27, 2021

The Metaflow GUI allows data scientists to monitor their workflows in real-time, track experiments, and see detailed logs and results for every executed task. link] Metaflow is a full-stack framework for data science that we started developing at Netflix over four years ago and which we open-sourced in 2019.

Python 89

Python Machine Learning Data Science Metadata 89

AltexSoft

NOVEMBER 7, 2022

Metadata database. A metadata database stores information about user permissions, past and current DAG and task runs, DAG configurations, and more. By default, Airflow handles metadata with SQLite which is meant for development only. building your own applications based on the Airflow web interface functionality.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

Monte Carlo

APRIL 20, 2023

I can surface ownership metadata and alert the relevant owners to make sure the appropriate changes are made so these breakages never happen. And over time, we’ve invested effort into cleaning up our lineages, simplifying our logic,” said SeatGeek Director of Data Engineering, Brian London.

Data Warehouse 52

Data Warehouse BI Data Government 52

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? For this purpose, you will have to build a 360-degree customer picture.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

Datakin

OCTOBER 14, 2021

dbt is an amazing way to transform data within a data warehouse. Run models & capture lineage metadata When working with Datakin (or any other OpenLineage backend) it’s important to generate the dbt docs first. Our schema has changed, and we want Datakin to have the latest metadata about tables and columns. %

Google Cloud 52

Google Cloud Datasets Bytes Metadata 52

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

Data Engineering Podcast

JANUARY 28, 2018

Your host is Tobias Macey and today I’m interviewing Danielle Robinson and Joe Hand about Dat Project, a distributed data sharing protocol for building applications of the future Interview Introduction How did you get involved in the area of data management? What is the Dat project and how did it get started?

Data 100

Data Project Electronics Data Management 100