ProjectPro

JANUARY 27, 2023

Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange. This indicates the growing use of the ETL process and various ETL tools and techniques across multiple industries.

BI 52

BI ETL Tools Retail Healthcare 52

Silectis

MARCH 9, 2021

This has been driven by the relatively recent emergence of “data engineering” as an organized discipline, the fact that data engineering is sometimes perceived as unglamorous relative to its cousin, data science, and the high hurdle for new entrants trying to become productive. – Demetri Kotsikopoulos , CEO of Silectis 3.

Data Engineering 52

Data Engineering Data Engineer Engineering ETL Tools 52

AltexSoft

OCTOBER 8, 2021

They are applied to retrieve data from the source systems, perform transformations when necessary, and load it into a target system ( data mart , data warehouse, or data lake). 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

AltexSoft

JANUARY 26, 2022

Since not all information can be useful as is, analytics engineers need to apply various transformations to different data pieces to ensure they correspond to given tasks. The ELT paradigm allows for loading raw data right into a cloud warehouse, data lake , or lakehouse , so transformations can happen afterward.

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

ProjectPro

FEBRUARY 18, 2022

ETL pipelines help data scientists to prepare data for analytics and business intelligence. Data from multiple systems (CRMs, social media platforms , Web reporting, etc.) Begin by exporting the raw sales data to AWS S3. Finally, use Tableau to show the cleansed and modified data in various graphs.

Project 52

Project AWS Kafka Healthcare 52

ProjectPro

NOVEMBER 30, 2021

ETL (Extract, Transform, and Load) Pipeline involves data extraction from multiple sources like transaction databases, APIs, or other business systems, transforming it, and loading it into a cloud-hosted database or a cloud data warehouse for deeper analytics and business intelligence.

Process 52

Process Data Pipeline Data Warehouse AWS 52

AltexSoft

SEPTEMBER 10, 2021

So, why does anyone need to integrate data in the first place? Today, companies want their business decisions to be driven by data. But here’s the thing — information required for business intelligence (BI) and analytics processes often lives in a breadth of databases and applications. Available resources.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

ProjectPro

JULY 27, 2021

Data engineers use the organizational data blueprint to collect, maintain and prepare the required data. Data architects require practical skills with data management tools including data modeling, ETL tools, and data warehousing.

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. Without context, streaming data is useless.” ” SSB enables users to configure data providers using out of the box connectors or their own connector to any data source.

Kafka 83

Kafka Manufacturing Data Lake SQL 83

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