Data Warehouse and Raw Data - Data Engineering Digest

The Hidden Threats in Your Data Warehouse Layers (And How to Fix Them)

Monte Carlo

AUGUST 6, 2024

Data warehouses are the centralized repositories that store and manage data from various sources. They are integral to an organization’s data strategy, ensuring data accessibility, accuracy, and utility. However, beneath their surface lies a host of invisible risks embedded within the data warehouse layers.

Data Warehouse

Data Warehouse Raw Data BI Machine Learning

Data Warehouse vs. Data Lake

Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. Read Many of the preferred platforms for analytics fall into one of these two categories.

Data Lake

Data Lake Data Warehouse Hadoop Raw Data

5 Helpful Extract & Load Practices for High-Quality Raw Data

Meltano

DECEMBER 7, 2022

Setting the Stage: We need E&L practices, because “copying raw data” is more complex than it sounds. For instance, how would you know which orders got “canceled”, an operation that usually takes place in the same data record and just “modifies” it in place. But not at the ingestion level.

Raw Data

Raw Data Metadata Data Database

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

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake

Data Lake Data Warehouse Unstructured Data Raw Data

Data Lakes vs. Data Warehouses

Grouparoo

JANUARY 11, 2022

This article looks at the options available for storing and processing big data, which is too large for conventional databases to handle. There are two main options available, a data lake and a data warehouse. What is a Data Warehouse? What is a Data Lake?

Data Lake

Data Lake Data Warehouse Unstructured Data Raw Data

Data Lake vs. Data Warehouse: Differences and Similarities

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. There are times when the data is structured , but it is often messy since it is ingested directly from the data source. What is Data Warehouse? . Data Warehouse in DBMS: .

Data Lake

Data Lake Data Warehouse Unstructured Data Amazon Web Services

How to get started with dbt

Christophe Blefari

MARCH 1, 2023

dbt Core is an open-source framework that helps you organise data warehouse SQL transformation. dbt was born out of the analysis that more and more companies were switching from on-premise Hadoop data infrastructure to cloud data warehouses. This switch has been lead by modern data stack vision.

Data Warehouse

Data Warehouse SQL Metadata Raw Data

Data Lake vs Data Warehouse - Working Together in the Cloud

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

Data Lake Data Warehouse Cloud Hadoop

Databricks, Snowflake and the future

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. Databricks is focusing on simplification (serverless, auto BI 2 , improved PySpark) while evolving into a data warehouse.

Metadata

Metadata Data Warehouse BI Scala

A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline

Data Pipeline Designing Data Lake Data Warehouse

Quick Guide to the Snowflake Semantic Layer in 2024

Hevo

AUGUST 29, 2024

Snowflake is a cloud data warehouse that has taken the world by storm, establishing itself as one of the core technologies in the cloud era. Central to its power is the Snowflake Semantic Layer, which helps organizations transform raw data into […]

Raw Data

Raw Data Data Warehouse AWS Cloud

Fact vs. Dimension Tables Explained

Monte Carlo

AUGUST 23, 2024

Fact tables are the core of a data warehouse, storing quantitative data for analysis. While fact tables provide the raw data for analysis, dimension tables offer the necessary context. These tables contain descriptive attributes that you can use to filter, group, and label the data stored in fact tables.

Data Warehouse

Data Warehouse Raw Data Datasets Business Intelligence

ELT Explained: What You Need to Know

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

Raw Data

Raw Data Data Warehouse Data Cleanse Data Integration

Best Practices for Migrating Historical Data to Snowflake

Snowflake

NOVEMBER 30, 2023

At TCS , we help companies shift their enterprise data warehouse (EDW) platforms to the cloud as well as offering IT services. We’re extremely familiar with just how tricky a cloud migration can be, especially when it involves moving historical business data. How many tables and views will be migrated, and how much raw data?

Data Warehouse

Data Warehouse Banking Data Cloud

Transforming Data with DBT BigQuery: A Comprehensive 101 Guide

Hevo

FEBRUARY 21, 2023

As data volumes continue to grow, organizations seek ways to make sense of it all, and data warehouses are at the center. BigQuery is a popular cloud-based data warehouse that allows for powerful analytics and querying at scale. This is […]

Raw Data

Raw Data Data Warehouse Data Cloud

Moving Past ETL and ELT: Understanding the EtLT Approach

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. Read More: What is ETL?

Data Lake

Data Lake ETL Tools Data Warehouse Data Pipeline

A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Towards Data Science

FEBRUARY 6, 2024

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

Systems

Systems Raw Data Metadata Data Cleanse

Q&A with Greg Rahn – The changing Data Warehouse market

Cloudera

DECEMBER 12, 2018

After having rebuilt their data warehouse, I decided to take a little bit more of a pointed role, and I joined Oracle as a database performance engineer. I spent eight years in the real-world performance group where I specialized in high visibility and high impact data warehousing competes and benchmarks.

Data Warehouse

Data Warehouse Relational Database Hadoop Database

Mastering DBT Snowflake: A 101 Beginner’s Guide to Building Robust Data Pipelines

Hevo

FEBRUARY 15, 2023

After the hustle and bustle of extracting data from multiple sources, you have finally loaded all your data to a single source of truth like the Snowflake data warehouse. However, data modeling is still challenging and critical for transforming your raw data into any analysis-ready form to get insights.

Data Pipeline

Data Pipeline Building Raw Data Data Warehouse

Setting up Data Lake on GCP using Cloud Storage and BigQuery

Analytics Vidhya

FEBRUARY 25, 2023

The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

Cloud Storage

Cloud Storage Data Lake Cloud Unstructured Data

13 dbt Commands You Should Start Using Today

Hevo

MARCH 31, 2023

dbt is a data transformation tool used by data engineers to process raw data within data warehouses. While dbt is a robust tool for transformation, you should be aware of the dbt commands to harness the power of dbt. Mastering dbt commands will let you run complex asks on dbt projects.

Raw Data

Raw Data Data Warehouse Data Engineering Data Engineer

How Do We Transform and Model Data at Cloud Academy?

Cloud Academy

JUNE 7, 2022

The data extraction process The first step of modeling and using data begins with extracting it from different sources and putting it in a library where it can be assessed: the Data Warehouse. In some cases, raw data is extracted and placed in the staging area; in others, a few transformations need to be performed.

Cloud

Cloud Data Warehouse Raw Data Business Intelligence

Data Curation Explained: How To Make Data More Valuable

Monte Carlo

JULY 25, 2023

What is data curation? Data curation is the process of transforming and enriching larger amounts of raw data into smaller, more widely accessible subsets of data that provide additional value to the organization or the intended use case. It would also make the data engineering team a bottleneck.

Raw Data

Raw Data Data Warehouse Data Architecture

Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

OCTOBER 28, 2024

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis.

Raw Data

Raw Data Datasets Aggregated Data Data Pipeline

Digital Transformation is a Data Journey From Edge to Insight

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Manufacturing

Manufacturing Data Warehouse Kafka Retail

5 Big Data Challenges in 2024

Knowledge Hut

MARCH 7, 2024

The greatest data processing challenge of 2024 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data. Inability to process large volumes of data Out of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it.

Big Data

Big Data Bytes Data Governance Raw Data

What is the ETL Process?

Grouparoo

DECEMBER 14, 2021

ETL, or Extract, Transform, Load, is a process that involves extracting data from different data sources , transforming it into more suitable formats for processing and analytics, and loading it into the target system, usually a data warehouse. ETL data pipelines can be built using a variety of approaches.

Process

Process Raw Data Data Warehouse Data Pipeline

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

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? The data vault paradigm addresses the desire to overlay organization on top of semi-permanent raw data storage.

Architecture

Architecture Raw Data Metadata Data Warehouse

ETL vs ELT Explained

Grouparoo

AUGUST 3, 2021

The mission of many data teams is a very simple one. They seek to use data to help the business take smarter actions. The input is raw data from everywhere that touches the business. How can we best get the data into a usable form? The end goal of both is to have data in a data warehouse ready to be leveraged.

Raw Data

Raw Data Data Warehouse SQL Coding

Deliver Personal Experiences In Your Applications With The Unomi Open Source Customer Data Platform

Data Engineering Podcast

DECEMBER 11, 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

Data Warehouse Raw Data Data Lake BI

Data News — Week 23.16

Christophe Blefari

APRIL 21, 2023

A lot of data teams embraced dbt, or at least the SQL with engineering practices to transform data in cloud data warehouses. As introduction Tristan gives the original vision of dbt that became mainstream, today. In dbt Core 1.5

Raw Data

Raw Data Data SQL Datasets

The Pros and Cons of Leading Data Management and Storage Solutions

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management

Data Management Management Data Lake Data Governance

The Pros and Cons of Leading Data Management and Storage Solutions

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management

Data Management Data Lake Management Data Governance

The Pros and Cons of Leading Data Management and Storage Solutions

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management

Data Management Management Data Lake Data Governance

What is ELT (Extract, Load, Transform)? A Beginner’s Guide [SQ]

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The data is loaded as-is, without any transformation.

Data Cleanse

Data Cleanse Raw Data Data Storage Data Warehouse

Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Data warehouse vs. data lake in a nutshell.

Data Lake

Data Lake Architecture IT Amazon Web Services

Mastering Batch Data Processing with Versatile Data Kit (VDK)

Towards Data Science

NOVEMBER 16, 2023

ELT comprises three main phases: Extract (E) : data is extracted from multiple sources in different formats, both structured and unstructured. Load (L) : data is loaded into a target destination, such as a data warehouse. Extract and Load This phase includes VDK jobs calling the Europeana REST API to extract raw data.

Data Process

Data Process Process Raw Data Data

Is the data warehouse going under the data lake?

ProjectPro

JULY 22, 2016

For the same cost, organizations can now store 50 times as much data as in a Hadoop data lake than in a data warehouse. Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why.

Data Lake

Data Lake Data Warehouse Hadoop Unstructured Data

7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More

Databand.ai

JULY 6, 2023

7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More Joseph Arnold July 6, 2023 What Are Data Pipelines? Data pipelines are a series of data processing steps that enable the flow and transformation of raw data into valuable insights for businesses.

Data Pipeline

Data Pipeline Data Science Raw Data Media

Implementing a Pharma Data Mesh using DataOps

DataKitchen

AUGUST 19, 2021

Each data source is updated on its own schedule, for example, daily, weekly or monthly. The DataKitchen Platform ingests data into a data lake and runs Recipes to create a data warehouse leveraged by users and self-service data analysts. Let’s consider how to break up our architecture into data mesh domains.

Pharmaceutical

Pharmaceutical Data Lake Data Warehouse Raw Data

ETL vs. ELT and the Evolution of Data Integration Techniques

Ascend.io

DECEMBER 14, 2022

ETL is unable to expand to keep and handle the volume of data we generate and collect today—at least not quickly or cost-effectively. In the hopes of resolving this issue, ETL tasks that update hundreds or millions of data warehouse tables frequently take place at night. This causes two issues.

Data Integration

Data Integration Raw Data Data Consolidation Data Warehouse

Data Warehousing Guide: Fundamentals & Key Concepts

Monte Carlo

FEBRUARY 15, 2023

Cloud data warehouses solve these problems. Belonging to the category of OLAP (online analytical processing) databases, popular data warehouses like Snowflake, Redshift and Big Query can query one billion rows in less than a minute. What is a data warehouse?

Data Warehouse

Data Warehouse Unstructured Data AWS Business Intelligence

How to Build a Data Pipeline in 6 Steps

Ascend.io

JANUARY 2, 2024

The key differentiation lies in the transformational steps that a data pipeline includes to make data business-ready. Ultimately, the core function of a pipeline is to take raw data and turn it into valuable, accessible insights that drive business growth. best suit our processed data? cleaning, formatting)?

Data Pipeline

Data Pipeline Building Raw Data Data Warehouse

Ripple's Data Evolution: Leveraging Databricks for Next-Gen XRP Ledger Analytics

Ripple Engineering

JULY 9, 2024

After evaluating numerous data solution providers, Databricks stood out due to its seamless performance and lakehouse capabilities, which offer the best of both data lakes and data warehouses. This vital information then streams to the XRPL Data Extractor App. Why Databricks Emerged as the Top Contender 1.

Hadoop

Hadoop Data Lake Machine Learning Raw Data

The Hidden Threats in Your Data Warehouse Layers (And How to Fix Them)

Data Warehouse vs. Data Lake

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5 Helpful Extract & Load Practices for High-Quality Raw Data

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

Data Lakes vs. Data Warehouses

Data Lake vs. Data Warehouse: Differences and Similarities

How to get started with dbt

Data Lake vs Data Warehouse - Working Together in the Cloud

Databricks, Snowflake and the future

A Guide to Data Pipelines (And How to Design One From Scratch)

Quick Guide to the Snowflake Semantic Layer in 2024

Fact vs. Dimension Tables Explained

ELT Explained: What You Need to Know

Best Practices for Migrating Historical Data to Snowflake

Transforming Data with DBT BigQuery: A Comprehensive 101 Guide

Moving Past ETL and ELT: Understanding the EtLT Approach

A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Q&A with Greg Rahn – The changing Data Warehouse market

Mastering DBT Snowflake: A 101 Beginner’s Guide to Building Robust Data Pipelines

Setting up Data Lake on GCP using Cloud Storage and BigQuery

13 dbt Commands You Should Start Using Today

How Do We Transform and Model Data at Cloud Academy?

Data Curation Explained: How To Make Data More Valuable

Complete Guide to Data Transformation: Basics to Advanced

Digital Transformation is a Data Journey From Edge to Insight

5 Big Data Challenges in 2024

What is the ETL Process?

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

ETL vs ELT Explained

Deliver Personal Experiences In Your Applications With The Unomi Open Source Customer Data Platform

Data News — Week 23.16

The Pros and Cons of Leading Data Management and Storage Solutions

The Pros and Cons of Leading Data Management and Storage Solutions

The Pros and Cons of Leading Data Management and Storage Solutions

What is ELT (Extract, Load, Transform)? A Beginner’s Guide [SQ]

Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

Mastering Batch Data Processing with Versatile Data Kit (VDK)

Is the data warehouse going under the data lake?

7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More

Implementing a Pharma Data Mesh using DataOps

ETL vs. ELT and the Evolution of Data Integration Techniques

Data Warehousing Guide: Fundamentals & Key Concepts

How to Build a Data Pipeline in 6 Steps

Ripple's Data Evolution: Leveraging Databricks for Next-Gen XRP Ledger Analytics

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