Building, Definition, Metadata and Raw Data

How to get started with dbt

Christophe Blefari

MARCH 1, 2023

In the ELT, the load is done before the transform part without any alteration of the data leaving the raw data ready to be transformed in the data warehouse. In a simple words dbt sits on top of your raw data to organise all your SQL queries that are defining your data assets.

Data Warehouse

Data Warehouse SQL Metadata Raw Data

Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

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

Data Lake Data Warehouse Unstructured Data Raw Data

What is dbt Testing? Definition, Best Practices, and More

Monte Carlo

AUGUST 30, 2023

Data testing is the first step in many data engineers’ journey toward reliable data. dbt (data build tool) is a SQL-based command-line tool that offers native testing features. Your test passes when there are no rows returned, which indicates your data meets your defined conditions.

SQL

SQL Datasets Database High Quality Data

Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

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Using Metrics Layer to Standardize and Scale Experimentation at DoorDash

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. Experimentation is one of the primary use cases that relies on metrics.

SQL

SQL Metadata Raw Data Government

Data Vault Architecture, Data Quality Challenges, And How To Solve Them

Monte Carlo

FEBRUARY 9, 2023

For those unfamiliar, data vault is a data warehouse modeling methodology created by Dan Linstedt (you may be familiar with Kimball or Imon models ) created in 2000 and updated in 2013. Data vault collects and organizes raw data as underlying structure to act as the source to feed Kimball or Inmon dimensional models.

Architecture

Architecture Raw Data Metadata Data Warehouse

How to Build a Mature dbt Project from Scratch

dbt Developer Hub

DECEMBER 5, 2021

We’ve also included some sample raw data to add to your warehouse so you can run these projects yourself! It's more important to think about how features build upon themselves (and each other) rather than how quickly they do so.* You can use this repository to benchmark the maturity of your own dbt project.

Project

Project Building Metadata BI

Ready or Not. The Post Modern Data Stack Is Coming.

Monte Carlo

MARCH 28, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Warehouse

Data Warehouse Raw Data Data Pipeline Software Engineer

AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. Why Use AWS Glue?

AWS

AWS Scala Metadata Data Lake

The Just-In-Time Revolution for Data-Driven Enterprises

The Modern Data Company

MARCH 1, 2024

Instead of forcing all data through a centralized bottleneck, they break it down into modular, self-contained units. Each Data Product is designed for a specific purpose, equipped with the necessary data, transformations, and metadata. Data Products simplify access and processing, empowering faster decision-making.

Database-centric

Database-centric Data Lake Government Retail

A Data Prediction for 2025

DataKitchen

FEBRUARY 2, 2023

Most data governance tools today start with the slow, waterfall building of metadata with data stewards and then hope to use that metadata to drive code that runs in production. In reality, the ‘active metadata’ is just a written specification for a data developer to write their code.

Metadata

Metadata BI Government ETL Tools

Data Engineering Weekly #114

Data Engineering Weekly

JANUARY 15, 2023

SiliconANGLE theCUBE: Analyst Predictions 2023 - The Future of Data Management By far one of the best analyses of trends in Data Management. 2023 predictions from the panel are; Unified metadata becomes kingmaker. RudderStack builds your CDP on top of your data warehouse, giving you a more secure and cost-effective solution.

Data Engineering

Data Engineering Data Engineer Engineering Metadata

Zero-ETL, ChatGPT, And The Future of Data Engineering

Towards Data Science

APRIL 3, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Engineering

Data Engineering Data Engineer Engineering Data Warehouse

The Complete Front-End Developer Roadmap 2024

Knowledge Hut

DECEMBER 29, 2023

Front-end development, or client-side development, involves building the User Interface (UI) of a website or a web application, that determines how every part of a website will look and how it will work. The “head” tags (<head> and </head>) contain the metadata or information about the website.

Portfolio

Portfolio Amazon Web Services Programming Language Coding

Data Contracts and Data Observability: Whatnot’s Full Circle Journey to Data Trust

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. Where do I log it?

Data

Data Metadata Software Engineer Software Engineering

How Airbnb Standardized Metric Computation at Scale

Airbnb Tech

JUNE 1, 2021

Because of this multi-year investment, when Airbnb’s business was severely disrupted by COVID-19 last year, we were able to quickly turn data into actionable insights and strategies. Anyone can look up definitions without confusion. Consistent : Data is always consistent. Declarative: Users define the “what” and not the “how”.

Datasets

Datasets Pipeline-centric Metadata Data Science

Get Your Analytics Insights Instantly – Without Abandoning Central IT

Cloudera

JANUARY 21, 2021

Building a Shadow IT organization with separate, disconnected data repositories that only serve your line of business’ needs and introduce compliance and security risk for your company, lose your organization’s focus and participation in core business, and see your company ending up paying much more in the end? Must you be: .

IT

IT Data Lake Data Warehouse Cloud Storage

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

MARCH 14, 2023

to build products and services for various purposes. A data stack, in turn, focuses on data : It helps businesses manage data and make the most out of it. Modern data stack architecture. While the open-source part is definitely a good thing, it requires a high level of expertise as there’s often no GUI.

IT

IT Data Warehouse Data Governance Data Lake

Data Collection for Machine Learning: Steps, Methods, and Best Practices

AltexSoft

JUNE 26, 2023

We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection? It’s the first and essential stage of data-related activities and projects, including business intelligence , machine learning , and big data analytics. How systems exchange data.

Data Collection

Data Collection Machine Learning Unstructured Data Non-relational Database

Unstructured Data: Examples, Tools, Techniques, and Best Practices

AltexSoft

MAY 12, 2023

By understanding these aspects comprehensively, you can harness the true potential of unstructured data and transform it into a strategic asset. What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization.

Unstructured Data

Unstructured Data NoSQL Hadoop Data Lake

Managing Big Data Quality And 4 Reasons To Go Smaller

Monte Carlo

JUNE 23, 2022

At the end of the day, managing big data quality is all about building the stack (and collecting the data) that’s right for your company – and there’s no one-size-fits all solution. The old big data quality paradigm held you needed to collect as much data as possible (it’s the new oil!)

Big Data

Big Data Management Machine Learning Data Warehouse

The Good and the Bad of Hadoop Big Data Framework

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop

Hadoop Big Data Google Cloud NoSQL

What is ETL Pipeline? Process, Considerations, and Examples

ProjectPro

NOVEMBER 30, 2021

If you are into Data Science or Big Data, you must be familiar with an ETL pipeline. This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. For example, build a calculator program that asks for an integer input.

Process

Process Data Pipeline Data Warehouse AWS

Case Study: Standard Cognition Uses Rockset to Deliver Data APIs and Real-Time Metrics for Vision AI

Rockset

JANUARY 28, 2020

Aside from video data from each camera-equipped store, Standard deals with other data sets such as transactional data, store inventory data that arrive in different formats from different retailers, and metadata derived from the extensive video captured by their cameras.

Retail

Retail Google Cloud Raw Data Data Lake

Real-Time Analytics in the World of Virtual Reality and Live Streaming

Rockset

SEPTEMBER 6, 2019

This raw data from the devices needs to be enriched with content metadata and geolocation information before it can be processed and analyzed. We don't have time to spend learning new APIs and building tools to extract data, and by exposing our data through SQL we enable our Data Analysts to build and run queries on live data.

Metadata

Metadata Kafka Data Cleanse SQL

The Good and the Bad of Databricks Lakehouse Platform

AltexSoft

MARCH 30, 2023

Shell, Adobe, Burberry, Columbia, Bayer — you definitely know the names. The answer is simple: They use the same technology to make the most of data. Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions.

Scala

Scala Data Lake BI Google Cloud

Data Lake vs Data Warehouse - Working Together in the Cloud

ProjectPro

AUGUST 11, 2021

The data warehouse layer consists of the relational database management system (RDBMS) that contains the cleaned data and the metadata, which is data about the data. The RDBMS can either be directly accessed from the data warehouse layer or stored in data marts designed for specific enterprise departments.

Data Lake

Data Lake Data Warehouse Cloud Hadoop

Dat: Distributed Versioned Data Sharing with Danielle Robinson and Joe Hand - Episode 16

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

Data Project Electronics Data Management

What is a Data Mesh — and How Not to Mesh it Up

Monte Carlo

JULY 30, 2020

Ask anyone in the data industry what’s hot these days and chances are “data mesh” will rise to the top of the list. But what is a data mesh and why should you build one? It does not have the same emphasis on decentralization and domain driven architecture as data mesh. Updated: January 2023.

IT

IT Data Lake Business Intelligence Data Architecture

Data Engineering Digest

How to get started with dbt

Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

Webinars

Trending Sources

What is dbt Testing? Definition, Best Practices, and More

Webinars

Using Metrics Layer to Standardize and Scale Experimentation at DoorDash

Data Vault Architecture, Data Quality Challenges, And How To Solve Them

How to Build a Mature dbt Project from Scratch

Ready or Not. The Post Modern Data Stack Is Coming.

AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

The Just-In-Time Revolution for Data-Driven Enterprises

A Data Prediction for 2025

Data Engineering Weekly #114

Zero-ETL, ChatGPT, And The Future of Data Engineering

The Complete Front-End Developer Roadmap 2024

Data Contracts and Data Observability: Whatnot’s Full Circle Journey to Data Trust

How Airbnb Standardized Metric Computation at Scale

Get Your Analytics Insights Instantly – Without Abandoning Central IT

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

Data Collection for Machine Learning: Steps, Methods, and Best Practices

Unstructured Data: Examples, Tools, Techniques, and Best Practices

Managing Big Data Quality And 4 Reasons To Go Smaller

The Good and the Bad of Hadoop Big Data Framework

What is ETL Pipeline? Process, Considerations, and Examples

Case Study: Standard Cognition Uses Rockset to Deliver Data APIs and Real-Time Metrics for Vision AI

Real-Time Analytics in the World of Virtual Reality and Live Streaming

The Good and the Bad of Databricks Lakehouse Platform

Data Lake vs Data Warehouse - Working Together in the Cloud

Dat: Distributed Versioned Data Sharing with Danielle Robinson and Joe Hand - Episode 16

What is a Data Mesh — and How Not to Mesh it Up

Top 100 Hadoop Interview Questions and Answers 2023

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