Clustering, Data Lakes and Hadoop - 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

Streaming Machine Learning Without a Data Lake

ODSC - Open Data Science

MAY 31, 2023

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

Data Lakes

Data Lakes Machine Learning Machine Learning Apache Kafka

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

Precisely

JANUARY 9, 2025

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. The big data boom was born, and Hadoop was its poster child. A data lake!

Data Warehouse

Data Warehouse Hadoop Data Governance Data Lakes

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Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

JULY 6, 2023

It supports various data types and offers advanced features like data sharing and multi-cluster warehouses. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It provides a scalable and fault-tolerant ecosystem for big data processing.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

How to modernize data lakes with a data lakehouse architecture

IBM Journey to AI blog

JULY 5, 2023

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

Data Lakes

Data Lakes Data Warehouse Data Governance Analytics

How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

FEBRUARY 21, 2025

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. This also led to a backlog of data that needed to be ingested.

Data Science

Data Science AWS Hadoop Data Scientist

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

Flipboard

NOVEMBER 17, 2023

Set up a MongoDB cluster To create a free tier MongoDB Atlas cluster, follow the instructions in Create a Cluster. Delete the MongoDB Atlas cluster. Prior joining AWS, as a Data/Solution Architect he implemented many projects in Big Data domain, including several data lakes in Hadoop ecosystem.

K-nearest Neighbors

K-nearest Neighbors AWS Clustering Database

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 ? Thus ensuring optimal performance.

Hadoop

Hadoop SQL Big Data Big Data

Why Open Table Format Architecture is Essential for Modern Data Systems

phData

NOVEMBER 8, 2024

Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. However, this feature becomes an absolute must-have if you are operating your analytics on top of your data lake or lakehouse. It can also be integrated into major data platforms like Snowflake.

Data Lakes

Data Lakes Data Warehouse Database Azure

Unleashing the power of Presto: The Uber case study

IBM Journey to AI blog

SEPTEMBER 25, 2023

When a query is constructed, it passes through a cost-based optimizer, then data is accessed through connectors, cached for performance and analyzed across a series of servers in a cluster. Because of its distributed nature, Presto scales for petabytes and exabytes of data.

Data Lakes

Data Lakes Analytics Analytics Clustering

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

AWS Machine Learning Blog

DECEMBER 18, 2023

Prerequisites For this solution we use MongoDB Atlas to store time series data, Amazon SageMaker Canvas to train a model and produce forecasts, and Amazon S3 to store data extracted from MongoDB Atlas. The following screenshots shows the setup of the data federation. Setup the Database access and Network access.

Clustering

Clustering AWS Database ML

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. Data lakes and cloud storage provide scalable solutions for large datasets.

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. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data

Big Data Big Data Data Lakes Apache Hadoop

What is Snowpark — and Why Does it Matter? A phData Perspective

phData

SEPTEMBER 20, 2023

By offering native data engineering functionality inside of Snowflake virtual warehouses, we see a thriving data ecosystem being built around Snowflake. The release of Snowpark makes our customers’ lives simpler by unifying their data lake into a complete data platform.

SQL

SQL Python Data Lakes Machine Learning

Big Data Syllabus: A Comprehensive Overview

Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

Big Data

Big Data Big Data Big Data Analytics Big Data Analytics

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

Pickl AI

SEPTEMBER 17, 2024

Technologies and Tools for Big Data Management To effectively manage Big Data, organisations utilise a variety of technologies and tools designed specifically for handling large datasets. This section will highlight key tools such as Apache Hadoop, Spark, and various NoSQL databases that facilitate efficient Big Data management.

Big Data

Big Data Big Data Big Data Analytics Big Data Analytics

Discover the Most Important Fundamentals of Data Engineering

Pickl AI

NOVEMBER 4, 2024

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. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

Introduction to Apache NiFi and Its Architecture

Pickl AI

JULY 30, 2024

Flexibility : NiFi supports a wide range of data sources and formats, allowing organizations to integrate diverse systems and applications seamlessly. Scalability : NiFi can be deployed in a clustered environment, enabling organizations to scale their data processing capabilities as their data needs grow.

ETL

ETL Data Lakes Big Data Big Data

Understanding Business Intelligence Architecture: Key Components

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence

Business Intelligence Business Intelligence ETL Data Lakes

How to Effectively Handle Unstructured Data Using AI

DagsHub

NOVEMBER 11, 2024

Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data. These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. This will ensure the data is in an ideal structure for further analysis.

AI

AI AI Data Lakes Database

How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning

Machine Learning Machine Learning Data Lakes AI

How data engineers tame Big Data?

Dataconomy

FEBRUARY 23, 2023

Data engineers are responsible for designing and building the systems that make it possible to store, process, and analyze large amounts of data. These systems include data pipelines, data warehouses, and data lakes, among others. However, building and maintaining these systems is not an easy task.

Big Data

Big Data Big Data Data Engineering Data Engineer

What are the Biggest Challenges with Migrating to Snowflake?

phData

FEBRUARY 5, 2024

Setting up the Information Architecture Setting up an information architecture during migration to Snowflake poses challenges due to the need to align existing data structures, types, and sources with Snowflake’s multi-cluster, multi-tier architecture. Get to know all the ins and outs of your upcoming migration.

SQL

SQL Database Data Quality Data Warehouse

Top Big Data Tools Every Data Professional Should Know

Pickl AI

FEBRUARY 23, 2025

Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. By harnessing the power of Big Data tools, organisations can transform raw data into actionable insights that foster innovation and competitive advantage.

Big Data

Big Data Big Data Apache Hadoop Apache Kafka

Data Science Current

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

Streaming Machine Learning Without a Data Lake

Webinars

Trending Sources

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

Webinars

Essential data engineering tools for 2023: Empowering for management and analysis

How to modernize data lakes with a data lakehouse architecture

How Rocket Companies modernized their data science solution on AWS

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

Unfolding the Details of Hive in Hadoop

Why Open Table Format Architecture is Essential for Modern Data Systems

Unleashing the power of Presto: The Uber case study

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

A Comprehensive Guide to the main components of Big Data

A Comprehensive Guide to the Main Components of Big Data

What is Snowpark — and Why Does it Matter? A phData Perspective

Big Data Syllabus: A Comprehensive Overview

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

Discover the Most Important Fundamentals of Data Engineering

Introduction to Apache NiFi and Its Architecture

Understanding Business Intelligence Architecture: Key Components

How to Effectively Handle Unstructured Data Using AI

How to Manage Unstructured Data in AI and Machine Learning Projects

How data engineers tame Big Data?

What are the Biggest Challenges with Migrating to Snowflake?

Top Big Data Tools Every Data Professional Should Know

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