Dataconomy

MAY 26, 2025

As businesses increasingly rely on data to drive strategies and decisions, effective management of this information becomes essential for achieving competitive advantage and insights. Platforms and tools Organizations often rely on advanced tools such as Apache Hadoop and Apache Spark to streamline data handling.

Big Data 91

Big Data Big Data Apache Hadoop Data Quality 91

Women in Big Data

OCTOBER 9, 2024

With the explosive growth of big data over the past decade and the daily surge in data volumes, it’s essential to have a resilient system to manage the vast influx of information without failures. Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Smart Data Collective

MAY 20, 2019

How will we manage all this information? For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. What’s more interesting, however, are the trends formed as a result of the newer digitally-reliant solutions.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. ETL Design Pattern Here is an example of how the ETL design pattern can be used in a real-world scenario: A healthcare organization wants to analyze patient data to improve patient outcomes and operational efficiency.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

The goal is to ensure that data is available, reliable, and accessible for analysis, ultimately driving insights and informed decision-making within organisations. Their work ensures that data flows seamlessly through the organisation, making it easier for Data Scientists and Analysts to access and analyse information.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52