Data Science Dojo

JULY 6, 2023

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. Apache Hadoop: Apache Hadoop is an open-source framework for distributed storage and processing of large datasets.

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

ODSC - Open Data Science

SEPTEMBER 27, 2023

Cost-Efficiency By leveraging cost-effective storage solutions like the Hadoop Distributed File System (HDFS) or cloud-based storage, data lakes can handle large-scale data without incurring prohibitive costs. Processing: Relational databases are optimized for transactional processing and structured queries using SQL.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements. Types of ETL Tools.

ETL 93

ETL Hadoop Data Warehouse Data Pipeline 93

Smart Data Collective

MAY 20, 2019

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. Basic Business Intelligence Experience is a Must. Communication happens to be a critical soft skill of business intelligence.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Pickl AI

NOVEMBER 15, 2023

It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. Transactional databases, containing operational data generated by day-to-day business activities, feed into the Data Warehouse for analytical processing.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

IBM Journey to AI blog

JANUARY 18, 2023

Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. This adds an additional ETL step, making the data even more stale. Data platform architecture has an interesting history. It was Datawarehouse.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52