Data Engineering Podcast

SEPTEMBER 11, 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. Interview Introduction How did you get involved in the area of data management?

Systems 100

Systems Metadata Building MongoDB 100

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

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

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Data Engineering Podcast

JANUARY 7, 2022

In this episode Metaplane founder Kevin Hu shares his working definition of the term and explains the work that he and his team are doing to cut down on the time to adoption for this new set of practices. 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

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

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

Christophe Blefari

JUNE 16, 2023

Last week I shared nice QR Code generated with ControlNet, this week someone released a model on HuggingFace to do it QR Code Conditioned ControlNet (not related to the Chinese original paper) and you can even use the generator web UI. Stakeholders often notice data issues before the data team does. This is metrics drift.

Programming Language 130

Programming Language SQL PostgreSQL Data 130

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

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

Monte Carlo

MARCH 28, 2023

Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Monte Carlo joined the fray and said, “Maybe having engineers manually code unit tests isn’t the best way to ensure data quality.” It’s important to note the data is still in a relatively raw state.

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Data Engineering Podcast

MAY 18, 2021

RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Interview Introduction How did you get involved in the area of data management?

Metadata 100

Metadata Kafka Data Warehouse Hadoop 100

ProjectPro

FEBRUARY 8, 2023

It offers users a data integration tool that organizes data from many sources, formats it, and stores it in a single repository, such as data lakes, data warehouses, etc., Glue uses ETL jobs for extracting data from various AWS cloud services and integrating it into data warehouses and lakes.

AWS 98

AWS Scala Metadata Data Lake 98

Monte Carlo

JANUARY 23, 2024

Every team has their own analytic domain they own, forcing decentralized team structures around broadly standardized definitions of what “good” data looks like. The data warehouse is the mirror of the organization in many ways. Editing data like code. “If Data debt or data pipeline chaos.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

Monte Carlo

FEBRUARY 9, 2023

Over the past several years, data warehouses have evolved dramatically, but that doesn’t mean the fundamentals underpinning sound data architecture needs to be thrown out the window. What is a Data Vault model? Pie Insurance , a leading small business insurtech, leverages a data vault 2.0

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

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. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Data Warehouse 100

Data Warehouse Data Lake BI Business Intelligence 100

Towards Data Science

APRIL 3, 2023

Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Monte Carlo joined the fray and said, “Maybe having engineers manually code unit tests isn’t the best way to ensure data quality.” It’s important to note the data is still in a relatively raw state.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

Monte Carlo

FEBRUARY 26, 2024

A data catalog is a constantly updated inventory of the universe of data assets within an organization. It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems.

Metadata 52

Metadata Government Data Data Governance 52

Data Engineering Podcast

SEPTEMBER 19, 2021

He also discusses the technical underpinnings of what he is building and how using the data warehouse as a shared resource drastically shortens the time required to see value. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data.

Machine Learning 100

Machine Learning Data Warehouse Banking Metadata 100

dbt Developer Hub

DECEMBER 19, 2023

Integration points ​ Discovery API (formerly referred to as Metadata API) Overview — This GraphQL API allows you to query the metadata that dbt Cloud generates every time you run a dbt project. The job level will only provide you the metadata of one job, giving you only a small snapshot of part of the project.

Metadata 52

Metadata Cloud Accessible Accessibility 52

Maxime Beauchemin

JANUARY 20, 2017

The data engineering discipline took cues from its sibling, while also defining itself in opposition, and finding its own identity. Like data scientists, data engineers write code. They’re highly analytical, and are interested in data visualization.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Ascend.io

FEBRUARY 28, 2024

This process reduces noise in the data, which is crucial for the effectiveness of AI algorithms, especially in complex predictive models and deep learning applications. Such comprehensive metadata management is crucial in adhering to privacy and compliance standards, safeguarding AI operations against potential legal and ethical pitfalls.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

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

dbt Developer Hub

JANUARY 24, 2023

CI is a software development practice that ensures we automatically test our code prior to merging into another branch. This job will only run and test the code that's been modified within that specific pull request. In addition, it can use the same Slim CI job definition to run separate pull requests in parallel.

Metadata 52

Metadata Python Cloud Data Warehouse 52

Monte Carlo

FEBRUARY 8, 2024

Vendors who specialize in the detection of data reliability issues did not perform as well as the others.” G2 Crowd G2 was one of the earliest non-vendor resources to put together a credible list of data observability vendors and a definition for the category. How does it impact data warehouse/lakehouse performance?

BI 59

BI Data Pipeline Data Warehouse Data 59

dbt Developer Hub

JULY 26, 2022

With the dbt Semantic Layer, this definition would live in dbt and the logic to create the dataset for that metric would be consistent across all different consuming experiences. Best of all, definition changes would get reflected in downstream tools, so you no longer need to manually search and update every downstream dependency.

Metadata 52

Metadata BI Datasets SQL 52

Monte Carlo

JANUARY 4, 2024

Over the last two years, data contracts have become one of most talked about (and hotly debated ) topics in data engineering. And like any new — and admittedly abstract — concept, there are several different definitions of data contracts floating around on LinkedIn , Medium , and Substack.

Data 40

Data Metadata Software Engineer Software Engineering 40

Towards Data Science

FEBRUARY 15, 2023

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture. data:/data -./src:/src data:/data All the code is available in this GitHub repository.

Data Lake 77

Data Lake Data Warehouse Hadoop Data Architecture 77

Knowledge Hut

JULY 24, 2023

The ability of a DBMS to change its schema definition at one level without affecting the schema definition at the next level is called data independence. But why do we need data integrity in a DBMS? In addition to data entered by users, database systems typically store large amounts of data.

Database Design 52

Database Design Relational Database Metadata Database 52

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

AltexSoft

NOVEMBER 23, 2021

Before we get into more detail, let’s determine how data virtualization is different from another, more common data integration technique — data consolidation. Data virtualization vs data consolidation. The example of a typical two-tier architecture with a data lake and data warehouses and several ETL processes.

Process 69

Process Data Lake Metadata Data Warehouse 69

Netflix Tech

OCTOBER 18, 2022

We want users to rely on shared templates and reuse their workflow definitions across their team, saving time and effort on creating the same functionality. Maestro high level architecture In Maestro, a workflow is a DAG (Directed acyclic graph) of individual units of job definition called Steps.

Java 81

Java Data Machine Learning Systems 81

ProjectPro

JANUARY 24, 2023

In addition to databases running on AWS, Glue can automatically find structured and semi-structured data kept in your data lake on Amazon S3, data warehouse on Amazon Redshift, and other storage locations. 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 Weekly

MARCH 24, 2023

It leads to a need where a developer should instrument multiple code paths to generate the ride-sharing event. As the developers frequently change the code, it can introduce various bugs in the data generation code. About Schemata Schemata is a schema modeling framework for decentralized domain-driven ownership of data.

Engineering 67

Engineering Data Transportation Database 67

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59