ProjectPro

JULY 13, 2015

Large commercial banks like JPMorgan have millions of customers but can now operate effectively-thanks to big data analytics leveraged on increasing number of unstructured and structured data sets using the open source framework - Hadoop. We can zoom in and see the impact.”-

Hadoop 52

Hadoop Big Data Data Analytics Banking 52

Edureka

JUNE 1, 2023

This is where AWS Data Analytics comes into action, providing businesses with a robust, cloud-based data platform to manage, integrate, and analyze their data. In this blog, we’ll explore the world of Cloud Data Analytics and a real-life application of AWS Data Analytics.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

DataKitchen

APRIL 11, 2023

He drew from his twenty-five years of experience in business analytics, pharmaceutical brand launch strategy, and project management. He also highlighted the importance of agility and adaptability in data analytics. It is essential to recognize the evolution of the field and the changing expectations of data consumers.

Data Analytics 52

Data Analytics Pharmaceutical Data Analysis Consulting 52

ProjectPro

MARCH 12, 2016

For the leading payment network - PayPal, Big Data is an asset and is used for serious business strategies. Big Data Analytics and Data Science is at the heart of all this processing in the 17-year-old PayPal. At PayPal the raw clickstream data is processed in Hadoop through a cleaning phase.

Big Data 52

Big Data Data Analytics Hadoop Algorithm 52

Snowflake

SEPTEMBER 19, 2023

With all the hoopla around AI, there’s a lot to get up to speed on—especially the implications this technology has for data analytics. Ahmad has not only witnessed the emergence of AI in the data space but helped shape it throughout his career. AI unlocks new data use cases. Some takeaways?

Machine Learning 121

Machine Learning Unstructured Data Data Analytics Government 121

Rockset

JULY 29, 2022

This fast, serverless, highly scalable, and cost-effective multi-cloud data warehouse has built-in machine learning, business intelligence, and geospatial analysis capabilities for querying massive amounts of structured and semi-structured data. This is true for the three data warehouses mentioned above.

Data Analytics 52

Data Analytics Data Warehouse Datasets Cloud 52

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 112

IT Unstructured Data Data Architecture Government 112

Data Engineering Weekly

MAY 5, 2024

[link] Miles McBain: Patterns and anti-patterns of data analysis reuse We practiced and discussed reusability in software engineering, but I never thought deeply about data analytics reuse. For me, It is always adding additional dimensions in a dashboard to bring reusability, but that won’t be the case in ad-hoc analytics.

Data Engineering 68

Data Engineering Data Engineer Engineering Google Cloud 68

Cloudera

APRIL 1, 2024

This recognition underscores Cloudera’s commitment to continuous customer innovation and validates our ability to foresee future data and AI trends, and our strategy in shaping the future of data management. Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics.

Cloud 105

Cloud Unstructured Data Metadata Government 105

Towards Data Science

JANUARY 16, 2024

Image by the author 2010 to 2020 - The Cloud Data Warehouse Enterprises now wanted quick data analytics without yesterday’s constraints of flexibility, processing power and scale. Examples include: Amazon Redshift, Google BigQuery, Snowflake, Azure Synapse Analytics, Databricks etc. So what was missing?

Data Warehouse 95

Data Warehouse Data Architecture Relational Database NoSQL 95

Cloudera

SEPTEMBER 29, 2021

Through processing vast amounts of structured and semi-structured data, AI and machine learning enabled effective fraud prevention in real-time on a national scale. . Data can be used to solve many problems faced by governments, and in times of crisis, can even save lives. .

Medical 83

Medical Government Hospitality Electronics 83

Knowledge Hut

APRIL 25, 2024

Most businesses today understand how to gather the terabytes of data that constantly pour into their operations and utilize analytics to transform them into insightful information. Given its advantages, big data and analytics are crucial for any business trying to maximize its commercial potential.

Big Data 97

Big Data Entertainment Transportation Banking 97

Propel Data

OCTOBER 11, 2022

The main difference between data lakes and data warehouses is data lakes allow unstructured data, but data warehouses need structured data.

Data Lake 40

Data Lake Data Warehouse Unstructured Data Data Analytics 40

Striim

SEPTEMBER 11, 2024

In an ETL-based architecture, data is first extracted from source systems, then transformed into a structured format, and finally loaded into data stores, typically data warehouses. This method is advantageous when dealing with structured data that requires pre-processing before storage.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

U-Next

MARCH 1, 2023

Data lakes contain raw, unstructured data of an organization, which can be stored indefinitely – either immediately or in the future. Predefined business needs are analyzed based on clean and processed structured data that has been cleaned and processed using structured data warehouses.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Cloudera

SEPTEMBER 15, 2022

Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

Systems 87

Systems Hadoop Metadata Telecommunication 87

Snowflake

MARCH 9, 2023

And after all that money and effort, 85% of data projects still fail. Formed in 2022, the company provides a simple, SaaS-based drag and drop interface that democratizes AI data analytics, allowing everyone within the business to solve problems and create value faster. Wand is on a mission to change that.

Cloud 89

Cloud Unstructured Data Data Data Storage 89

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Knowledge Hut

MAY 2, 2024

To store and process even only a fraction of this amount of data, we need Big Data frameworks as traditional Databases would not be able to store so much data nor traditional processing systems would be able to process this data quickly. It can deliver near real-time analytics.

Hadoop 96

Hadoop Scala Datasets Java 96

Edureka

APRIL 14, 2025

Meanwhile, the visualization tool offers wide-ranging data connectors—from Azure SQL and SharePoint to Salesforce and Google Analytics—enabling quick access to structured and semi-structured data. However, it leans more toward transforming and presenting cleaned data rather than processing raw datasets.

BI 40

BI Business Intelligence Raw Data Retail 40

Snowflake

MARCH 14, 2024

Glue provides a simple, direct way for organizations with SAP systems to quickly and securely ingest SAP data into Snowflake. It sits on the application layer within SAP, which makes almost any structured data accessible and available for change data capture (CDC).

IT 121

IT Data Ingestion Data AWS 121

Sync Computing

NOVEMBER 7, 2024

Despite these limitations, data warehouses, introduced in the late 1980s based on ideas developed even earlier, remain in widespread use today for certain business intelligence and data analysis applications. While data warehouses are still in use, they are limited in use-cases as they only support structured data.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Knowledge Hut

FEBRUARY 29, 2024

Currently, numerous resources are being created on the internet consisting of data science websites, data analytics websites, data science portfolio websites, data scientist portfolio websites and so on. So, having the right knowledge of tools and technology is important for handling such data.

Data Science 98

Data Science Datasets Machine Learning Database Design 98

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

Knowledge Hut

APRIL 23, 2024

Big data and data mining are neighboring fields of study that analyze data and obtain actionable insights from expansive information sources. Big data encompasses a lot of unstructured and structured data originating from diverse sources such as social media and online transactions.

Data Mining 52

Data Mining Big Data Database-centric Unstructured Data 52

Netflix Tech

OCTOBER 27, 2020

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy.

Data Warehouse 100

Data Warehouse Datasets Data Big Data 100

Knowledge Hut

APRIL 23, 2024

Data storing and processing is nothing new; organizations have been doing it for a few decades to reap valuable insights. Compared to that, Big Data is a much more recently derived term. So, what exactly is the difference between Traditional Data and Big Data?

Big Data 52

Big Data Relational Database Data Structured Data 52

U-Next

SEPTEMBER 7, 2022

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. As a result, a data lake concept becomes a game-changer in the field of big data management. . Data is kept in its.raw format. Different Storage Options .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

AltexSoft

AUGUST 4, 2021

A data warehouse (DW) is a data repository that allows for storing and managing all the historical enterprise data, coming from disparate internal and external sources like CRMs, ERPs, flat files, etc. Initially, DWs dealt with structured data presented in tabular forms. Subject-focused data analytics.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

ProjectPro

AUGUST 11, 2021

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Precisely

DECEMBER 19, 2022

Read more > #4 Transform Context into Intelligence in the PropTech Industry Data powers all analytics today – driving industry workflows from customer and market intelligence to understand property features, risk management, and resource allocation. Will signage have adequate visibility to drivers as they pass by?

Data Governance 52

Data Governance Retail Government High Quality Data 52

Knowledge Hut

DECEMBER 29, 2023

Data Mining Data science field of study, data mining is the practice of applying certain approaches to data in order to get useful information from it, which may then be used by a company to make informed choices. It separates the hidden links and patterns in the data. Data mining's usefulness varies per sector.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Monte Carlo

JUNE 6, 2023

Now, let’s take a closer look at the strengths and weaknesses of the most popular data quality team structures. Data engineering Having the data engineering team lead the response to data quality is by far the most common pattern. It is deployed by about half of all organizations that use a modern data stack.

Data Governance 98

Data Governance Government Data Data Engineering 98

Towards Data Science

JUNE 8, 2023

Now, let’s take a closer look at the strengths and weaknesses of the most popular data quality team structures. Data engineering Photo by Luke Chesser on Unsplash Having the data engineering team lead the response to data quality is by far the most common pattern.

Data Governance 98

Data Governance Government Generalist Data Engineering 98

AltexSoft

JUNE 7, 2021

The framework provides a way to divide a huge data collection into smaller chunks and shove them across interconnected computers or nodes that make up a Hadoop cluster. As a result, a Big Data analytics task is split up, with each machine performing its own little part in parallel. Data management and monitoring options.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52