Women in Big Data

OCTOBER 9, 2024

Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes. Refer to Unlocking the Power of Big Data Article to understand the use case of these data collected from various sources.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

SEPTEMBER 19, 2024

Summary: This article compares Spark vs Hadoop, highlighting Spark’s fast, in-memory processing and Hadoop’s disk-based, batch processing model. Introduction Apache Spark and Hadoop are potent frameworks for big data processing and distributed computing. What is Apache Hadoop?

Hadoop 52

Hadoop Big Data Big Data Clustering 52

Precisely

MARCH 9, 2023

In this article, we’ll focus on a data lake vs. data warehouse. We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Big Data 59

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

This article explores the key fundamentals of Data Engineering, highlighting its significance and providing a roadmap for professionals seeking to excel in this vital field. Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. Now let’s get into the main topic of the article. It allows you to write a reusable piece of code that can be called from anywhere in your queries.

SQL 52

SQL Database Apache Hadoop Data Science 52