AltexSoft

SEPTEMBER 10, 2021

To get a single unified view of all information, companies opt for data integration. In this article, you will learn what data integration is in general, key approaches and strategies to integrate siloed data, tools to consider, and more. What is data integration and why is it important?

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

Hevo

OCTOBER 17, 2024

ETL stands for Extract, Transform, and Load. ETL is a process of transferring data from various sources to target destinations/data warehouses and performing transformations in between to make data analysis ready. Managing data is a tedious task if done manually and leads to no guarantee of accuracy.

ETL Tools 40

ETL Tools Data Warehouse Data Analysis Process 40

Data Engineering Podcast

JANUARY 15, 2022

StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change.

Engineering 100

Engineering Electronics ETL Tools Data Pipeline 100

ProjectPro

FEBRUARY 8, 2023

ETL stands for Extract, Transform, and Load, which involves extracting data from various sources, transforming the data into a format suitable for analysis, and loading the data into a destination system such as a data warehouse. ETL developers play a significant role in performing all these tasks.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 52

Ascend.io

DECEMBER 21, 2022

The last three years have seen a remarkable change in data infrastructure. ETL changed towards ELT. Now, data teams are embracing a new approach: reverse ETL. Cloud data warehouses, such as Snowflake and BigQuery, have made it simpler than ever to combine all of your data into one location.

ETL Tools 52

ETL Tools ETL System Data Warehouse Data Consolidation 52

ProjectPro

FEBRUARY 16, 2023

Data Engineers and Data Scientists require efficient methods for managing large databases, which is why centralized data warehouses are in high demand. Cloud computing has made it easier for businesses to move their data to the cloud for better scalability, performance, solid integrations, and affordable pricing.

AWS 52

AWS Data Management ETL Tools Management 52

Data Engineering Weekly

DECEMBER 29, 2022

Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. The modern(?)

Metadata 62

Metadata Data Warehouse ETL Tools Data Workflow 62

Towards Data Science

NOVEMBER 4, 2023

Often it is a data warehouse solution (DWH) in the central part of our infrastructure. Data warehouse exmaple. Introduction to Apache Iceberg Tables Simplified data integrations Managed solutions like Fivetran and Stitch were built to manage third-party API integrations with ease.

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

AltexSoft

AUGUST 4, 2021

Now let’s think of sweets as the data required for your company’s daily operations. Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

Grouparoo

OCTOBER 26, 2021

A data pipeline typically consists of three main elements: an origin, a set of processing steps, and a destination. Data pipelines are key in enabling the efficient transfer of data between systems for data integration and other purposes. Let’s take a closer look at some of the major components of a data pipeline.

Data Pipeline 52

Data Pipeline ETL Tools Data Warehouse ETL System 52

AltexSoft

OCTOBER 30, 2021

Data engineer’s integral task is building and maintaining data infrastructure — the system managing the flow of data from its source to destination. This typically includes setting up two processes: an ETL pipeline , which moves data, and a data storage (typically, a data warehouse ), where it’s kept.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Grouparoo

DECEMBER 14, 2021

The ETL data integration process has been around for decades and is an integral part of data analytics today. In this article, we’ll look at what goes on in the ETL process and some modern variations that are better suited to our modern, data-driven society. What is ETL?

Process 52

Process Raw Data Data Warehouse Data Pipeline 52

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

Knowledge Hut

MARCH 28, 2024

Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance. Develop data models, data governance policies, and data integration strategies. Familiarity with ETL tools and techniques for data integration.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Grouparoo

FEBRUARY 9, 2022

Today, organizations are adopting modern ETL tools and approaches to gain as many insights as possible from their data. However, to ensure the accuracy and reliability of such insights, effective ETL testing needs to be performed. So what is an ETL tester’s responsibility? Data integration testing.

Process 52

Process ETL System Data Warehouse Metadata 52

Knowledge Hut

OCTOBER 31, 2023

The key distinctions between the two jobs are outlined in the following table: Parameter AWS Data Engineer Azure Data Engineer Platform Amazon Web Services (AWS) Microsoft Azure Data Services AWS Glue, Redshift, Kinesis, etc. Azure Data Factory, Databricks, etc.

Data Engineering 52

Data Engineering Data Engineer AWS Engineering 52

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

ProjectPro

SEPTEMBER 30, 2014

The conventional ETL software and server setup are plagued by problems related to scalability and cost overruns, which are ably addressed by Hadoop. If you encounter Big Data on a regular basis, the limitations of the traditional ETL tools in terms of storage, efficiency and cost is likely to force you to learn Hadoop.

Hadoop 52

Hadoop ETL Tools Unstructured Data ETL System 52

DareData

JULY 5, 2023

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Data Engineering Weekly

APRIL 16, 2023

Redshift is no longer a true competitor in the warehouse space. 1 On dbt; Yes, I completely agree with the author’s take that it lacks the basic functionality expected of a best-in-class tool. Meta shares its ever-changing landscape of data engineering. Airflow is obsolete. Airbyte is not production-grade software. #1

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 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. process data in real time and run streaming analytics. In other words, Kafka can serve as a messaging system, commit log, data integration tool, and stream processing platform.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

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 Raw Data 52

Knowledge Hut

NOVEMBER 19, 2023

Top 10 Azure Data Engineer Tools I have compiled a list of the most useful Azure Data Engineer Tools here, please find them below. Azure Data Factory Azure Data Factory is a cloud ETL tool for scale-out serverless data integration and data transformation.

Data Engineering 52

Data Engineering Data Engineer Engineering PostgreSQL 52

Monte Carlo

SEPTEMBER 12, 2022

And at IMPACT, Maxime shared his key predictions for the future of data engineering. Watch his entire talk here , or read on for five key takeaways about the post-modern data stack. Now, according to Maxime, a new trend is emerging that could have a similar effect on data engineering workloads: reverse ETL.

BI 52

BI Data Governance ETL Tools Data Warehouse 52

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 extraction process requires careful planning to ensure data integrity.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 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 Machine Learning BI 64

Ascend.io

SEPTEMBER 19, 2023

In this article, we’ll delve into what is an automated ETL pipeline, explore its advantages over traditional ETL, and discuss the inherent benefits and characteristics that make it indispensable in the data engineering toolkit. What Is an Automated ETL Pipeline? Read More: What is ETL? The result?

ETL Tools 52

ETL Tools ETL Method Data Engineering Data Engineer 52

Edureka

JULY 3, 2024

As data volumes explode across enterprises, the struggle to manage, integrate, and analyze it is getting real. Thankfully, with serverless data integration solutions like Azure Data Factory (ADF), data engineers can easily orchestrate, integrate, transform, and deliver data at scale.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

Edureka

JULY 4, 2024

It’s a Swiss Army knife for data pros, merging data integration, warehousing, and big data analytics into one sleek package. In other words, Synapse lets users ingest, prepare, manage, and serve data for immediate BI and machine learning needs. Is Azure Synapse an ETL tool? No worries.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

ProjectPro

JANUARY 24, 2023

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. You can leverage AWS Glue to discover, transform, and prepare your data for analytics.

AWS 52

AWS Data Lake ETL Tools Scala 52

Knowledge Hut

JANUARY 29, 2024

Here are some common examples: Merging Data Sources : Combining data from multiple sources into one cohesive dataset for analysis, facilitating comprehensive insights. Cleaning Data: Removing irrelevant or unnecessary data, ensuring that only pertinent information is used for analysis. What are data wrangling challenges?

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Knowledge Hut

NOVEMBER 17, 2023

Data Modeling The process of creating a logical and physical data model for a system is known as data modeling. Understanding data modeling concepts like entity-relationship diagrams, data normalization, and data integrity is a requirement for an Azure Data Engineer.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

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

Databand.ai

JULY 5, 2023

They help organizations understand the dependencies between data sources, processes, and systems, enabling better data governance and impact analysis. They provide insights into the health of data integration processes, detect issues in real-time, and enable teams to optimize data flows.

Data Pipeline 40

Data Pipeline Data Lake Data Warehouse Datasets 40

Edureka

JULY 16, 2024

Introduction Amazon Redshift, a cloud data warehouse service from Amazon Web Services (AWS), will directly query your structured and semi-structured data with SQL. Amazon Redshift Serverless allows customers to analyze and query data without configuring and managing a data warehouse.

AWS 40

AWS Data Warehouse Amazon Web Services Business Intelligence 40

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. Validate data integrity at key stages to maintain accuracy throughout your flow.

Data Preparation 40

Data Preparation Process BI ETL Tools 40

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineer performs a multi-faceted role in an organization by identifying, extracting, and delivering the data sets in useful formats. You shall know database creation, data manipulation, and similar operations on the data sets. Your organization will use internal and external sources to port the data.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52