Alation

FEBRUARY 20, 2020

Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data. Organizationally the innovation of self-service analytics, pioneered by Tableau and Qlik, fundamentally transformed the user model for data analysis. Disruptive Trend #1: Hadoop.

Data Lakes

ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

Data Lakes

Pickl AI

AUGUST 1, 2023

Acquire essential skills to efficiently preprocess data before it enters the data pipeline. Hadoop: The Definitive Guide by Tom White This comprehensive guide delves into the Apache Hadoop ecosystem, covering HDFS, MapReduce, and big data processing.

Data Engineering

O'Reilly Media

MAY 15, 2025

Theres a lot of heavy lifting on the data infrastructure that they need to have in place. Its similar to Big Data, where the enterprise took longer to optimize their stack. 7:19 : Can you describe why enterprises need to modernize their data stack? Theres a lot of complexity in data and how data is handled.

AI

IBM Journey to AI blog

JULY 5, 2023

The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale. Data may be stored in its raw original form or optimized into a different format suitable for consumption by specialized engines. Comprehensive data security and data governance (i.e.

Data Lakes

Pickl AI

MAY 29, 2024

Mastering programming, statistics, Machine Learning, and communication is vital for Data Scientists. A typical Data Science syllabus covers mathematics, programming, Machine Learning, data mining, big data technologies, and visualisation. What does a typical Data Science syllabus cover?

Data Scientist

Pickl AI

JUNE 7, 2024

It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. Definition and Explanation of the ETL Process ETL is a data integration method that combines data from multiple sources.

ETL

Pickl AI

MAY 31, 2023

Tools such as Matplotlib, Seaborn, and Tableau may help you in creating useful visualisations that make challenging data more readily available and understandable to others. It is critical for knowing how to work with huge data sets efficiently. Yes, kids, especially teenagers can be the ideal starting age for learning Data Science.

Data Science

DagsHub

OCTOBER 23, 2024

Here are some challenges you might face while managing unstructured data: Storage consumption: Unstructured data can consume a large volume of storage. For instance, if you are working with several high-definition videos, storing them would take a lot of storage space, which could be costly.

Machine Learning

Alation

JANUARY 25, 2022

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric. ” 1.

DataOps

Data Science Blog

MARCH 14, 2023

Big Data tauchte als Buzzword meiner Recherche nach erstmals um das Jahr 2011 relevant in den Medien auf. Big Data wurde zum Business-Sprech der darauffolgenden Jahre. In der Parallelwelt der ITler wurde das Tool und Ökosystem Apache Hadoop quasi mit Big Data beinahe synonym gesetzt.

Big Data

Data Science Blog

MAY 15, 2023

Aber Moment mal, was ist eigentlich ein Data Lakehouse? Der Artikel beginnt mit einer Definition, was ein Lakehouse ist, gibt einen kurzen geschichtlichen Abriss, wie das Lakehouse entstanden ist und zeigt, warum und wie man ein Data Lakehouse aufbauen sollte.

Data Warehouse

Dataconomy

MAY 20, 2025

Definition and purpose The primary purpose of ensemble modeling is to combine multiple predictive models to maximize accuracy and minimize error rates. Technological advancements Improvements in big data technologies, such as Hadoop and Spark, have transformed the landscape of ensemble modeling.

Decision Trees