Big Data Analytics, Data Lakes and Database

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

Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

APRIL 2, 2019

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

Data Lakes

Data Lakes Big Data Big Data Data Scientist

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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10 Things AWS Can Do for Your SaaS Company

Smart Data Collective

FEBRUARY 20, 2022

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. This article finally gets to the core question we started with: what can AWS do for your SaaS business?

AWS

AWS Cloud Computing Data Lakes Database

Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

SEPTEMBER 4, 2023

Text analytics is crucial for sentiment analysis, content categorization, and identifying emerging trends. Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data.

Analytics

Analytics Analytics Big Data Analytics Big Data Analytics

Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. The sample dataset Upload the dataset to Amazon S3 and crawl the data to create an AWS Glue database and tables.

Machine Learning

Machine Learning Machine Learning Data Governance ML

Unstructured data management and governance using AWS AI/ML and analytics services

Flipboard

OCTOBER 25, 2023

Why it’s challenging to process and manage unstructured data Unstructured data makes up a large proportion of the data in the enterprise that can’t be stored in a traditional relational database management systems (RDBMS). These services write the output to a data lake.

AWS

AWS ML ML Analytics

5 Best Practices for Extracting, Analyzing, and Visualizing Data

Smart Data Collective

DECEMBER 13, 2022

There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Data lakes hold raw data that has not yet been altered to meet a specific purpose.

Data Analysis

Data Analysis Data Analysis Analytics Analytics

Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS

AWS Data Lakes Clustering Data Preparation

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.

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.

Big Data

Big Data Big Data Data Lakes Apache Hadoop

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

Pickl AI

SEPTEMBER 17, 2024

The importance of Big Data lies in its potential to provide insights that can drive business decisions, enhance customer experiences, and optimise operations. Organisations can harness Big Data Analytics to identify trends, predict outcomes, and make informed decisions that were previously unattainable with smaller datasets.

Big Data

Big Data Big Data Big Data Analytics Big Data Analytics

Big Data Syllabus: A Comprehensive Overview

Pickl AI

AUGUST 9, 2024

Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities. Businesses need to analyse data as it streams in to make timely decisions. This diversity requires flexible data processing and storage solutions.

Big Data

Big Data Big Data Big Data Analytics Big Data Analytics

How to Effectively Handle Unstructured Data Using AI

DagsHub

NOVEMBER 11, 2024

Types of Unstructured Data As unstructured data grows exponentially, organisations face the challenge of processing and extracting insights from these data sources. Unlike structured data, unstructured data doesn’t fit neatly into predefined models or databases, making it harder to analyse using traditional methods.

AI

AI AI Data Lakes Database

Understanding Business Intelligence Architecture: Key Components

Pickl AI

JANUARY 28, 2025

They encompass all the origins from which data is collected, including: Internal Data Sources: These include databases, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and flat files within an organization. Data can be structured (e.g., databases), semi-structured (e.g.,

Business Intelligence

Business Intelligence Business Intelligence ETL Data Lakes

Azure Data Engineer Jobs

Pickl AI

APRIL 6, 2023

Sound knowledge of relational databases or NoSQL databases like Cassandra. An example of the Azure Data Engineer Jobs in India can be evaluated as follows: 6-8 years of experience in the IT sector. Data Warehousing concepts and knowledge should be strong. Knowledge in using Azure Data Factory Volume.

Azure

Azure Data Engineer Data Engineering Data Engineering

How Netflix Applies Big Data Across Business Verticals: Insights and Strategies

Pickl AI

SEPTEMBER 18, 2024

Read More: How Airbnb Uses Big Data and Machine Learning to Offer World-Class Service Netflix’s Big Data Infrastructure Netflix’s data infrastructure is one of the most sophisticated globally, built primarily on cloud technology. petabytes of data. How Does Netflix Ensure Security Against Fraud?

Big Data

Big Data Big Data Apache Kafka Big Data Analytics

Unfolding the Details of Hive in Hadoop

Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. Schema-on-Read Unlike traditional databases, Hive follows a schema-on-read approach. It applies the data structure during querying rather than data ingestion. Is Hive ACID-compliant?

Hadoop

Hadoop SQL Big Data Big Data

Your Complete Roadmap to Become an Azure Data Scientist

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

Azure

Azure Data Scientist Data Science Machine Learning

Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

AWS Machine Learning Blog

NOVEMBER 12, 2024

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

AWS

AWS AI AI Data Lakes

Azure Data Engineer Portfolio Project Series For Beginners (Part-I)

Towards AI

NOVEMBER 15, 2024

As you can see on the left side of the above image, there are many services like AI + Machine Learning, Analytics, Compute, Containers, Databases, DevOps, Integration, Networking, Security, Storage, and many more categories of resources. Now you can see the Data storage option. Here we need to create ADLS.

Azure

Azure Data Engineer Data Engineering Data Engineering

Top Big Data Tools Every Data Professional Should Know

Pickl AI

FEBRUARY 23, 2025

Summary: Big Data tools empower organizations to analyze vast datasets, leading to improved decision-making and operational efficiency. Ultimately, leveraging Big Data analytics provides a competitive advantage and drives innovation across various industries.

Big Data

Big Data Big Data Apache Hadoop Apache Kafka

Data Science Current

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

Differentiating Between Data Lakes and Data Warehouses

Webinars

Trending Sources

Here’s Why Automation For Data Lakes Could Be Important

Webinars

10 Things AWS Can Do for Your SaaS Company

Beyond data: Cloud analytics mastery for business brilliance

Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

Unstructured data management and governance using AWS AI/ML and analytics services

5 Best Practices for Extracting, Analyzing, and Visualizing Data

Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler

A Comprehensive Guide to the main components of Big Data

A Comprehensive Guide to the Main Components of Big Data

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

Big Data Syllabus: A Comprehensive Overview

How to Effectively Handle Unstructured Data Using AI

Understanding Business Intelligence Architecture: Key Components

Azure Data Engineer Jobs

How Netflix Applies Big Data Across Business Verticals: Insights and Strategies

Unfolding the Details of Hive in Hadoop

Your Complete Roadmap to Become an Azure Data Scientist

Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

Azure Data Engineer Portfolio Project Series For Beginners (Part-I)

Top Big Data Tools Every Data Professional Should Know

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