Data Engineering Podcast

SEPTEMBER 11, 2022

In this episode he shares the design of the project and how it fits into your development practices. 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.

Systems 100

Systems Metadata Building MongoDB 100

Christophe Blefari

MARCH 1, 2023

You can also add metadata on models (in YAML). In a nutshell the dbt journey starts with sources definition on which you will define models that will transform these sources to something else you'll need in your downstream usage of the data. You can read dbt's official definitions. examples on BigQuery ).

Data Warehouse 278

Data Warehouse SQL Metadata Raw Data 278

Monte Carlo

AUGUST 25, 2023

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. One advantage of data warehouses is their integrated nature.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Data Engineering Podcast

NOVEMBER 9, 2020

Summary A data catalog is a critical piece of infrastructure for any organization who wants to build analytics products, whether internal or external. While there are a number of platforms available for building that catalog, many of them are either difficult to deploy and integrate, or expensive to use at scale.

Building 100

Building PostgreSQL BI Metadata 100

Christophe Blefari

JANUARY 20, 2024

Every company out there has his own definition for the data engineer role. The idea behind is to solve data problem by building software. What is data engineering As I said it before data engineering is still a young discipline with many different definitions. Who are the data engineers? Is it really modern?

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Data Engineering Podcast

MAY 22, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. Can you describe the architecture of Flyte?

Machine Learning 130

Machine Learning Data Engineering Data Engineer Cloud 130

Netflix Tech

DECEMBER 3, 2022

This is one way to build trust with our internal user base. Obviously not all tools are made with the same use case in mind, so we are planning to add more code samples for other (than classical batch ETL) data processing purposes, e.g. Machine Learning model building and scoring. backfill.sch.yaml ??? daily.sch.yaml ???

Data Pipeline 143

Data Pipeline Scala Metadata Food 143

Data Engineering Podcast

JULY 24, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

Data Engineering 100

Data Engineering Data Engineer Lambda Architecture Engineering 100

Ascend.io

APRIL 22, 2024

A unified platform that combines a powerful metadata core, an extensible plugin architecture, DataAware automation, and multiple AI Assistants. But as they add more data sources, implement real-time streaming pipelines, and build out a modern data lake, the complexity compounds quickly.

Project 52

Project Metadata Data Pipeline Data Engineering 52

Monte Carlo

AUGUST 30, 2023

dbt (data build tool) is a SQL-based command-line tool that offers native testing features. In dbt, the built-in schema test `not_null` can be used to build completeness checks. There are some limits to keep in mind as you build a dbt testing discipline. And one of the most popular approaches is dbt testing.

SQL 52

SQL Datasets Database High Quality Data 52

Netflix Tech

FEBRUARY 16, 2021

The original SKU catalog is a logic-heavy client library packaged with complex metadata configuration files and consumed by various services. Operational Efficiency: The majority of the changes require metadata configuration files and library code changes, usually taking days of testing and service release to adopt the updates.

Building 83

Building Management Metadata Entertainment 83

Zalando Engineering

OCTOBER 18, 2023

The query directives are generally useful for clients to express certain types of metadata for the query. It is only used in our GraphQL linter that executes during the build time and prevents additions of new values to enums which are marked as final. We will discuss this in detail in the next section. are some entities.

Metadata 86

Metadata Coding Banking Designing 86

Lyft Engineering

OCTOBER 3, 2023

Data quality is an amorphous term, with various definitions depending on the context. In Verity, we defined data quality as follows: Verity’s Definition of Data Quality The measure of how well data can be used as intended. Five aspects of data quality with the definition in italics and an example in quotes.

Big Data 84

Big Data Metadata Data Warehouse Data 84

LinkedIn Engineering

APRIL 25, 2023

In this blog post, we will cover how the GraphQL layer is architected for use by our internal engineers to build member and customer facing applications. We had two choices in hand - invest more in Deco to address the pressing issues, or adopt GraphQL and build the necessary infrastructure around it for our needs. Given that our Rest.li

Architecture 109

Architecture Metadata Java Transportation 109

Marc Lamberti

SEPTEMBER 22, 2023

Digging into the official definition, you will see that Airflow is noted as an open-source platform that programmatically authors, schedules, and monitors workflows. As long as you know Python, you can build data pipelines and fully utilize AIrflow. Airflow stores metadata in it (DAG runs, XComs, Task instances, etc.

Data Pipeline 130

Data Pipeline Python Metadata Database 130

DoorDash Engineering

APRIL 12, 2023

The Metrics Layer, also known as a Semantic Layer, is a critical component of the modern data stack that has recently received significant industry attention offers a powerful solution to the challenge of standardizing metric definitions. We will also dive deep into our design and implementation processes and the lessons we learnt.

SQL 82

SQL Metadata Raw Data Government 82

Netflix Tech

MARCH 25, 2019

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

Building 87

Building Metadata Transportation Data Ingestion 87

LinkedIn Engineering

JULY 19, 2023

Co-Authors: Sumedh Sakdeo , Lei Sun , Sushant Raikar , Stanislav Pak , and Abhishek Nath Introduction At LinkedIn, we build and operate an open source data lakehouse deployment to power Analytics and Machine Learning workloads. While functional, our current setup for managing tables is fragmented.

Big Data 78

Big Data Data Management Management Metadata 78

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. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow.

BI 100

BI Data Warehouse Metadata SQL 100

Cloudera

OCTOBER 7, 2021

The concept of the data mesh architecture is not entirely new; Its conceptual origins are rooted in the microservices architecture, its design principles (i.e., Data and Metadata: Data inputs and data outputs produced based on the application logic. Key Design Principles of a Data Mesh.

Architecture 124

Architecture Metadata Government Kafka 124

Cloudera

FEBRUARY 8, 2024

It has been designed and developed as an open community standard to ensure compatibility across languages and implementations. Note that the materialized view definition contains the ‘stored by iceberg’ clause. The snapshotId of the source tables involved in the materialized view are also maintained in the metadata.

Metadata 90

Metadata Data Warehouse BI AWS 90

Towards Data Science

NOVEMBER 4, 2023

How to Become a Data Engineer As a data engineer, I am tasked to design efficient data processes almost every day. Indeed, why would we build a data connector from scratch if it already exists and is being managed in the cloud? Typical Airflow architecture includes a schduler based on metadata, executors, workers and tasks.

Data Engineering 83

Data Engineering Data Engineer Engineering BI 83

François Nguyen

MARCH 22, 2021

How do we build data products ? This is really for us the definition of a self serve platform. ” Code : all the code necessary to build a data product (data pipelines, API, policies). As you can see, this is in the code part where you are building your data pipelines, a misnomer because this is an over simplification.

Technology 130

Technology Architecture Google Cloud Metadata 130

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. Atlas provides open metadata management and governance capabilities to build a catalog of all assets, and also classify and govern these assets.

Data Governance 95

Data Governance Government Metadata Datasets 95

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. What is the current state of the ecosystem for generating and sharing metadata between systems? What are your goals for the OpenLineage effort?

Metadata 100

Metadata Kafka Data Warehouse Hadoop 100

DoorDash Engineering

DECEMBER 5, 2023

Key terminology Here are definitions and links to further documentation about the tools we used. Exposes a unique hardware definition language (HDL) for configuring compute resources. Designed for monitoring application and infrastructure metrics. Figure 3 illustrates the high level architecture of the future design.

Kafka 91

Kafka Programming Language Metadata Architecture 91

Netflix Tech

JANUARY 10, 2024

Since then, the video pipeline has undergone substantial improvements and broad expansions: Starting with Standard Dynamic Range (SDR) at Standard-Definitions , we expanded the encoding pipeline to 4K and High Dynamic Range (HDR) which enabled support for our premium offering. divide the input video into small chunks 2.

Process 91

Process Pipeline-centric Media Metadata 91

AltexSoft

AUGUST 22, 2022

In this post, we’ll attempt to explain the idea behind a data fabric, its architectural building blocks, the benefits it brings, and ways to approach its implementation. It is a general design approach that leverages data virtualization principles. Data fabric architecture building blocks. Data and metadata.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Data Engineering Podcast

SEPTEMBER 19, 2021

Summary Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. 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.

Machine Learning 100

Machine Learning Data Warehouse Banking Metadata 100

Data Engineering Podcast

FEBRUARY 6, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. How does it work? How does it work?

Data Pipeline 100

Data Pipeline Data Metadata Software Engineer 100

Towards Data Science

JUNE 16, 2023

Identifying your business-critical dashboards Looker exposes metadata about content usage in pre-built Explores that you can enrich with your own data to make it more useful. How to keep your critical data model definitions updated Automate as much as possible around tagging your critical data models. critical, non-critical).

BI 77

BI Data ETL Tools Machine Learning 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. The system stores metadata about data which makes it easier to find and retrieve data. But why do we need data integrity in a DBMS? Physical Data Independence in DBMS 1.

Database Design 52

Database Design Relational Database Metadata Database 52

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

Cloudera

NOVEMBER 13, 2020

We’ll discuss the various phases Impala takes a query through and how small query optimizations are incorporated into the design of each phase. Query Planner Design. Metadata Caching. The new Catalog design means that Impala coordinators will only load the metadata that they need instead of a full snapshot of all the tables.

Metadata 143

Metadata Coding SQL Database 143

LinkedIn Engineering

JUNE 26, 2023

These self-service integrations are typically owned by the teams that build and support the underlying infrastructure. Just as important, a growing number of use cases are driven by developers building applications. This means that developers frequently encounter gaps in self-service, requiring them to build their own solutions.

Data Pipeline 112

Data Pipeline Kafka MySQL SQL 112

Monte Carlo

FEBRUARY 9, 2023

architecture (with some minor deviations) to achieve their data integration objectives around scalability and use of metadata. “A The other advantage is because we follow a standard design, we are able to generate a lot of our code using code templates and metadata. methodology. 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. Before we get started, can you each give your definition of what "DataOps" means to you?

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100