ETL Tools, Raw Data and Structured Data

ETL Tools

Raw Data

Structured Data

Data Vault on Snowflake: Feature Engineering and Business Vault

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. The transformations we apply under feature engineering prepares the data for ML model training.

Engineering

Engineering Raw Data Data Science Machine Learning

Mastering the Art of ETL on AWS for Data Management

ProjectPro

FEBRUARY 16, 2023

Data integration with ETL has evolved from structured data stores with high computing costs to natural state storage with read operation alterations thanks to the agility of the cloud. Data integration with ETL has changed in the last three decades. One of the key benefits of using ETL on AWS is Scalability.

AWS

AWS Data Management ETL Tools Management

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Webinars

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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Trending Sources

Confessions of a Data Guy

What is Data Extraction? Examples, Tools & Techniques

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools

ETL Tools Database-centric Data Mining Raw Data

Webinars

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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Top ETL Use Cases for BI and Analytics:Real-World Examples

ProjectPro

JANUARY 27, 2023

It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange.

BI ETL Tools Retail Healthcare

What Is Data Wrangling? Examples, Benefits, Skills and Tools

Knowledge Hut

JANUARY 29, 2024

In today's data-driven world, where information reigns supreme, businesses rely on data to guide their decisions and strategies. However, the sheer volume and complexity of raw data from various sources can often resemble a chaotic jigsaw puzzle.

Raw Data

Raw Data Data Mining Data Preparation Structured Data

Moving Past ETL and ELT: Understanding the EtLT Approach

Ascend.io

AUGUST 31, 2023

Performance: Because the data is transformed and normalized before it is loaded , data warehouse engines can leverage the predefined schema structure to tune the use of compute resources with sophisticated indexing functions, and quickly respond to complex analytical queries from business analysts and reports.

Data Lake

Data Lake Data Warehouse ETL Tools Data Pipeline

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 sources can be broadly classified into three categories.

Data Lake

Data Lake Architecture IT Amazon Web Services

Difference between Pig and Hive-The Two Key Components of Hadoop Ecosystem

ProjectPro

OCTOBER 15, 2014

Generally data to be stored in the database is categorized into 3 types namely Structured Data, Semi Structured Data and Unstructured Data. 2) Hive Hadoop Component is used for completely structured Data whereas Pig Hadoop Component is used for semi structured data.

Hadoop

Hadoop Java Unstructured Data SQL

Data Warehousing Guide: Fundamentals & Key Concepts

Monte Carlo

FEBRUARY 15, 2023

A company’s production data, third-party ads data, click stream data, CRM data, and other data are hosted on various systems. An ETL tool or API-based batch processing/streaming is used to pump all of this data into a data warehouse. The following diagram explains how integrations work.

Data Warehouse

Data Warehouse Unstructured Data AWS Business Intelligence

Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

DECEMBER 7, 2021

It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline.

Data Pipeline

Data Pipeline Architecture Kafka AWS

What is AWS EMR (Amazon Elastic MapReduce)?

Edureka

JULY 4, 2024

For example, a retail company might use EMR to process high volumes of transaction data from hundreds or thousands of different sources (point-of-sale systems, online sales platforms, and inventory databases). Arranging the raw data could composite a 360-degree view of your sales customer integration across all channels.

AWS

AWS Amazon Web Services Hadoop Big Data

The Good and the Bad of Databricks Lakehouse Platform

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

Scala Data Lake Machine Learning BI

Tableau Prep Builder: Streamline Your Data Preparation Process

Edureka

JULY 5, 2024

Tableau Prep has brought in a new perspective where novice IT users and power users who are not backward faithfully can use drag and drop interfaces, visual data preparation workflows, etc., simultaneously making raw data efficient to form insights. Frequently Asked Questions (FAQs) Is Tableau Prep an ETL tool?

Data Preparation

Data Preparation Process BI ETL Tools

Data Integration: Approaches, Techniques, Tools, and Best Practices for Implementation

AltexSoft

SEPTEMBER 10, 2021

With it, data is retrieved from its sources, migrated to a staging data repository where it undergoes cleaning and conversion to be further loaded into a target source (commonly data warehouses or data marts ). A newer way to integrate data into a centralized location is ELT.

Data Integration

Data Integration Data Consolidation Data Warehouse BI

100+ Data Engineer Interview Questions and Answers for 2023

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineer

Data Engineer Data Engineering Engineering Hadoop

Data Engineering Digest

Data Vault on Snowflake: Feature Engineering and Business Vault

Mastering the Art of ETL on AWS for Data Management

Webinars

Trending Sources

What is Data Extraction? Examples, Tools & Techniques

Webinars

Top ETL Use Cases for BI and Analytics:Real-World Examples

What Is Data Wrangling? Examples, Benefits, Skills and Tools

Moving Past ETL and ELT: Understanding the EtLT Approach

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

Difference between Pig and Hive-The Two Key Components of Hadoop Ecosystem

Data Warehousing Guide: Fundamentals & Key Concepts

Data Pipeline- Definition, Architecture, Examples, and Use Cases

What is AWS EMR (Amazon Elastic MapReduce)?

The Good and the Bad of Databricks Lakehouse Platform

Tableau Prep Builder: Streamline Your Data Preparation Process

Data Integration: Approaches, Techniques, Tools, and Best Practices for Implementation

100+ Data Engineer Interview Questions and Answers for 2023

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