Knowledge Hut

APRIL 19, 2023

It is the process of extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse. ETL is used to integrate data from different sources and formats into a single target for analysis. What is an ETL Pipeline?

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

Data Engineering Podcast

JANUARY 15, 2022

Summary Applications of data have grown well beyond the venerable business intelligence dashboards that organizations have relied on for decades. What are the core principles of data engineering that have remained from the original wave of ETL tools and rigid data warehouses?

Engineering 100

Engineering Electronics ETL Tools Data Pipeline 100

Knowledge Hut

JANUARY 30, 2024

This data can be structured, semi-structured, or entirely unstructured, making it a versatile tool for collecting information from various origins. The extracted data is then duplicated or transferred to a designated destination, often a data warehouse optimized for Online Analytical Processing (OLAP).

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Monte Carlo

SEPTEMBER 8, 2021

Modern data teams have all the right solutions in place to ensure that data is ingested, stored, transformed, and loaded into their data warehouse, but what happens at “the last mile?” In other words, how can data analysts and engineers ensure that transformed, actionable data is actually available to access and use?

ETL Tools 52

ETL Tools Data Warehouse BI Data Pipeline 52

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 64

Scala Data Lake BI Google Cloud 64

AltexSoft

OCTOBER 30, 2021

Such visualizations as graphs and charts are typically prepared by data analysts or business analysts, though not every project has those people employed. Then, a data scientist uses complex business intelligence tools to present business insights to executives. Managing data and metadata.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. 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.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

dbt Developer Hub

JUNE 12, 2023

Introduction ​ Most data modeling approaches for customer segmentation are based on a wide table with user attributes. This table only stores the current attributes for each user, and is then loaded into the various SaaS platforms via Reverse ETL tools.

Building 59

Building ETL Tools Data Warehouse Machine Learning 59

AltexSoft

JANUARY 26, 2022

Data engineers build data pipelines and perform ETL — extract data from sources, transform it, and load it into a centralized repository like a data warehouse. They aren’t normally burdened with the business side of the process. Here’s the video explaining how data engineers work.

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

ProjectPro

NOVEMBER 30, 2021

That's where the ETL (Extract, Transform, and Load) pipeline comes into the picture! Table of Contents What is ETL Pipeline? Data Pipeline - The Differences ETL Pipeline Architecture ETL Pipeline Tutorial - How to Build an ETL Pipeline? Now let us try to understand ETL data pipelines in more detail.

Process 52

Process Data Pipeline Data Warehouse AWS 52

AltexSoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

ProjectPro

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

FEBRUARY 18, 2022

Why is ETL used in Data Science? ETL stands for Extract, Transform, and Load. It entails gathering data from numerous sources, converting it, and then storing it in a new single data warehouse. ETL pipelines help data scientists to prepare data for analytics and business intelligence.

Project 52

Project AWS Kafka Healthcare 52

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

SEPTEMBER 26, 2021

Transform unstructured data in the form in which the data can be analyzed Develop data retention policies Skills Required to Become a Big Data Engineer Big Data Engineer Degree - Educational Background/Qualifications Bachelor’s degree in Computer Science, Information Technology, Statistics, or a similar field is preferred at an entry level.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

AltexSoft

SEPTEMBER 10, 2021

Data integration is the process of taking data from multiple disparate internal and external sources and putting it in a single location (e.g., data warehouse ) to achieve a unified view of collected data. So, why does anyone need to integrate data in the first place? Key types of data integration.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

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 Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Cloudera

JUNE 16, 2022

CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. Reduce ingest latency and complexity: Multiple point solutions were needed to move data from different data sources to downstream systems. Making the Lailas of the world successful: democratize streaming analytics with SQL.

Kafka 88

Kafka Manufacturing Data Lake SQL 88