Data Lakes, Hadoop and Information - Data Science Current

Data lakes vs. data warehouses: Decoding the data storage debate

Data Science Dojo

JANUARY 12, 2023

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

Data Lakes

Data Lakes Data Warehouse Hadoop Machine Learning

Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

SEPTEMBER 23, 2020

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

Data Lakes

Data Lakes Data Warehouse Big Data Big Data

Data Integrity for AI: What’s Old is New Again

Precisely

JANUARY 9, 2025

But the Internet and search engines becoming mainstream enabled never-before-seen access to unstructured content and not just structured data. Then came Big Data and Hadoop! The traditional data warehouse was chugging along nicely for a good two decades until, in the mid to late 2000s, enterprise data hit a brick wall.

Data Warehouse

Data Warehouse Hadoop Data Governance Data Lakes

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

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. Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format.

Data Lakes

Data Lakes Data Warehouse Database Big Data

Data Warehouse vs. Data Lake

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 Lakes

Data Lakes Data Warehouse Hadoop Big Data

Data Cataloging in the Data Lake: Alation + Kylo

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

Data Lakes Hadoop Tableau Big Data

8 Data Lake Vendors to Make Your Data Life Easier in 2023

ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

Data Lakes

Data Lakes Azure Data Warehouse Hadoop

How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

FEBRUARY 21, 2025

Thats why we use advanced technology and data analytics to streamline every step of the homeownership experience, from application to closing. Model training and scoring was performed either from Jupyter notebooks or through jobs scheduled by Apaches Oozie orchestration tool, which was part of the Hadoop implementation.

Data Science

Data Science AWS Hadoop Data Scientist

Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

Data Lakes

Data Lakes Data Warehouse Database ETL

Unfolding the Details of Hive in Hadoop

Pickl AI

JULY 6, 2023

Here comes the role of Hive in Hadoop. Hive is a powerful data warehousing infrastructure that provides an interface for querying and analyzing large datasets stored in Hadoop. In this blog, we will explore the key aspects of Hive Hadoop. What is Hadoop ?

Hadoop

Hadoop SQL Big Data Big Data

Big data

Dataconomy

FEBRUARY 25, 2025

Velocity Velocity describes the speed at which data is generated and processed. Big data systems often require real-time or near-real-time analysis to keep pace with the influx of new information.

Big Data

Big Data Big Data Data Lakes Machine Learning

Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Flipboard

NOVEMBER 17, 2023

The vector field should be represented as an array of numbers (BSON int32, int64, or double data types only). Refer to Review knnVector Type Limitations for more information about the limitations of the knnVector type. As a Data Engineer he was involved in applying AI/ML to fraud detection and office automation.

K-nearest Neighbors

K-nearest Neighbors AWS Clustering Database

Best 8 Data Version Control Tools for Machine Learning 2024

DagsHub

DECEMBER 11, 2023

Data auditing and compliance Almost each company face data protection regulations such as GDPR, forcing them to store certain information in order to demonstrate compliance and history of data sources. In this scenario, data versioning can help companies in both internal and external audits process.

Machine Learning

Machine Learning Machine Learning Data Lakes Database

Navigating the Big Data Frontier: A Guide to Efficient Handling

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. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline.

Big Data

Big Data Big Data Apache Kafka Data Pipeline

A Comprehensive Guide to the main components of Big Data

Pickl AI

DECEMBER 2, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Veracity Veracity refers to the trustworthiness and accuracy of the data.

Big Data

Big Data Big Data Data Lakes Apache Hadoop

A Comprehensive Guide to the Main Components of Big Data

Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Veracity Veracity refers to the trustworthiness and accuracy of the data.

Big Data

Big Data Big Data Data Lakes Apache Hadoop

Unleashing the power of Presto: The Uber case study

IBM Journey to AI blog

SEPTEMBER 25, 2023

Uber understood that digital superiority required the capture of all their transactional data, not just a sampling. They stood up a file-based data lake alongside their analytical database. Because much of the work done on their data lake is exploratory in nature, many users want to execute untested queries on petabytes of data.

Data Lakes

Data Lakes Analytics Analytics Clustering

Characteristics of Big Data: Types & 5 V’s of Big Data

Pickl AI

SEPTEMBER 17, 2024

The rise of Big Data has been fueled by advancements in technology that allow organisations to collect, store, and analyse vast amounts of information from diverse sources. The importance of Big Data lies in its potential to provide insights that can drive business decisions, enhance customer experiences, and optimise operations.

Big Data

Big Data Big Data Big Data Analytics Big Data Analytics

3 Major Trends at Strata New York 2017

DataRobot Blog

OCTOBER 3, 2017

Many announcements at Strata centered on product integrations, with vendors closing the loop and turning tools into solutions, most notably: A Paxata-HDInsight solution demo, where Paxata showcased the general availability of its Adaptive Information Platform for Microsoft Azure.

Data Lakes

Data Lakes Azure Data Pipeline Hadoop

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

DECEMBER 18, 2023

By exploring these challenges, organizations can recognize the importance of real-time forecasting and explore innovative solutions to overcome these hurdles, enabling them to stay competitive, make informed decisions, and thrive in today’s fast-paced business environment. For more information, refer to the following resources.

Clustering

Clustering AWS Database ML

Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. All phases of the data-information lifecycle.

Data Lakes

Data Lakes Data Warehouse Azure Apache Hadoop

Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala.

Data Science

Data Science Analytics Analytics Data Scientist

Discover the Most Important Fundamentals of Data Engineering

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. Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights.

Data Engineer

Data Engineer Data Engineering Data Engineering Data Engineering

Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

APRIL 21, 2025

Summary: Big Data refers to the vast volumes of structured and unstructured data generated at high speed, requiring specialized tools for storage and processing. Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions.

Big Data Syllabus: A Comprehensive Overview

Pickl AI

AUGUST 9, 2024

Volume It refers to the sheer amount of data generated daily, which can range from terabytes to petabytes. Organisations must develop strategies to store and manage this vast amount of information effectively. Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities.

Big Data

Big Data Big Data Big Data Analytics Big Data Analytics

Understanding Business Intelligence Architecture: Key Components

Pickl AI

JANUARY 28, 2025

Summary: Understanding Business Intelligence Architecture is essential for organizations seeking to harness data effectively. This framework includes components like data sources, integration, storage, analysis, visualization, and information delivery. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence

Business Intelligence Business Intelligence ETL Data Lakes

How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

OCTOBER 23, 2024

How to leverage Generative AI to manage unstructured data Benefits of applying proper unstructured data management processes to your AI/ML project. What is Unstructured Data? One thing is clear : unstructured data doesn’t mean it lacks information.

Machine Learning

Machine Learning Machine Learning Data Lakes AI

Shopping for Data

Alation

FEBRUARY 20, 2020

Self-service analytics environments are giving rise to the data marketplace. Unlike the data warehouses of the past, an enterprise data marketplace (EDM) doesn’t just store, process, or manage information by IT. Get the latest data cataloging news and trends in your inbox.

Data Warehouse

Data Warehouse Data Lakes Hadoop Data Preparation

How to Effectively Handle Unstructured Data Using AI

DagsHub

NOVEMBER 11, 2024

In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data? We only have the video without any information.

AI

AI AI Data Lakes Database

Data Catalogs for Search & Discovery

Alation

MARCH 29, 2021

A modern data catalog is more than just a collection of your enterprise’s every data asset. It’s also a repository of metadata — or data about data — on information sources from across the enterprise, including data sets, business intelligence reports, and visualizations.

Machine Learning

Machine Learning Machine Learning Data Lakes Hadoop

Introduction to Apache NiFi and Its Architecture

Pickl AI

JULY 30, 2024

With a user-friendly interface and robust features, NiFi simplifies complex data workflows and enhances real-time data integration. Overview In the era of Big Data , organizations inundated with vast amounts of information generated from various sources.

ETL

ETL Data Lakes Big Data Big Data

How data engineers tame Big Data?

Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

Big Data

Big Data Big Data Data Engineer Data Engineering

Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

JULY 8, 2024

It covers best practices for ensuring scalability, reliability, and performance while addressing common challenges, enabling businesses to transform raw data into valuable, actionable insights for informed decision-making. As stated above, data pipelines represent the backbone of modern data architecture.

Data Pipeline

Data Pipeline Data Quality Database Apache Kafka

What are the Biggest Challenges with Migrating to Snowflake?

phData

FEBRUARY 5, 2024

Walking you through the biggest challenges we have found when migrating our customer’s data from a legacy system to Snowflake. Background Information on Migrating to Snowflake So you’ve decided to move from your current data warehousing solution to Snowflake, and you want to know what challenges await you.

SQL

SQL Database Data Quality Data Warehouse

Popular Data Transformation Tools: Importance and Best Practices

Pickl AI

OCTOBER 10, 2024

It integrates well with cloud services, databases, and big data platforms like Hadoop, making it suitable for various data environments. Typical use cases include ETL (Extract, Transform, Load) tasks, data quality enhancement, and data governance across various industries.

Data Quality

Data Quality AWS Machine Learning Machine Learning

Top Big Data Tools Every Data Professional Should Know

Pickl AI

FEBRUARY 23, 2025

Introduction to Big Data Tools In todays data-driven world, organisations are inundated with vast amounts of information generated from various sources, including social media, IoT devices, transactions, and more. Big Data tools are essential for effectively managing and analysing this wealth of information.

Big Data

Big Data Big Data Apache Hadoop Apache Kafka

Data lakes vs. data warehouses: Decoding the data storage debate

Differentiating Between Data Lakes and Data Warehouses

Webinars

Trending Sources

Data Integrity for AI: What’s Old is New Again

Webinars

Data Version Control for Data Lakes: Handling the Changes in Large Scale

Data Warehouse vs. Data Lake

Data Cataloging in the Data Lake: Alation + Kylo

8 Data Lake Vendors to Make Your Data Life Easier in 2023

How Rocket Companies modernized their data science solution on AWS

Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Unfolding the Details of Hive in Hadoop

Big data

Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Best 8 Data Version Control Tools for Machine Learning 2024

Navigating the Big Data Frontier: A Guide to Efficient Handling

A Comprehensive Guide to the main components of Big Data

A Comprehensive Guide to the Main Components of Big Data

Unleashing the power of Presto: The Uber case study

Characteristics of Big Data: Types & 5 V’s of Big Data

3 Major Trends at Strata New York 2017

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

Data platform trinity: Competitive or complementary?

Data science vs data analytics: Unpacking the differences

Discover the Most Important Fundamentals of Data Engineering

Big Data vs. Data Science: Demystifying the Buzzwords

Big Data Syllabus: A Comprehensive Overview

Understanding Business Intelligence Architecture: Key Components

How to Manage Unstructured Data in AI and Machine Learning Projects

Shopping for Data

How to Effectively Handle Unstructured Data Using AI

Data Catalogs for Search & Discovery

Introduction to Apache NiFi and Its Architecture

How data engineers tame Big Data?

Build Data Pipelines: Comprehensive Step-by-Step Guide

What are the Biggest Challenges with Migrating to Snowflake?

Popular Data Transformation Tools: Importance and Best Practices

Top Big Data Tools Every Data Professional Should Know

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