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 118

IT Unstructured Data Data Architecture Government 118

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 Google Cloud 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 101

Machine Learning Unstructured Data Data Analytics Government 101

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 Engineer 69

Data Engineer Data Engineering Engineering Google Cloud 69

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 88

Medical Government Hospitality Electronics 88

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

Cloudera

NOVEMBER 15, 2024

However, there is a fundamental challenge standing in the way of being successful: data. Using Cloudera Data Flow and Cloudera Stream Processing, teams can filter, parse, normalize, and enrich log data in real time, ensuring that defenders are always working with clean, structured data that’s ready for advanced analytics.

Metadata 90

Metadata Unstructured Data Data Lake Government 90

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 92

Systems Hadoop Metadata Telecommunication 92

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

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

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 Engineer 75

Data Engineer Data Engineering Non-relational Database Engineering 75

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

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

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

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 Datasets 52

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 63

Data Warehouse Data Architecture Relational Database NoSQL 63

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

Knowledge Hut

APRIL 23, 2024

So, before you choose a field, it is essential to go for Business Intelligence and Visualization online certification and learn to turn data into opportunities with BI and visualization. The analytics domain gets classified into three categories, with data analytics being the broader term.

Business Intelligence 52

Business Intelligence Data Science BI 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 101

Data Warehouse Datasets Data Big Data 101

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 Pipeline-centric Machine Learning 52

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 99

IT Data Ingestion Data AWS 99

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

Knowledge Hut

DECEMBER 26, 2023

The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications. Data Engineers Data engineers are IT professionals whose responsibility is the preparation of data for operational or analytical use cases.

Data Science 97

Data Science BI Business Intelligence Machine Learning 97

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

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 Engineer 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 Data Lake Management 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

Rockset

MARCH 24, 2022

More so now than before, companies want to easily query data across different sources without worrying about data ops. It’s difficult to create data analytics systems that can easily do this while maintaining fast query performance and real-time capabilities.

Non-relational Database 52

Non-relational Database Relational Database Database Data Pipeline 52

Knowledge Hut

JUNE 16, 2023

In today's data-driven world, organizations are trying to find valuable insights from the vast sets of data available to them. That is where Data analytics comes into the picture - guiding organizations to make smarter decisions by utilizing statistical and computational methods. What is Data Analytics?

Programming Language 52

Programming Language Data Science Data Analytics Cloud Computing 52

Knowledge Hut

JUNE 28, 2023

In our data-driven world, our lives are governed by big data. The TV shows we watch, the social media we follow, the news we read, and even the optimized routes we take to work are all influenced by the power of big data analytics. Focus Exploration and discovery of hidden patterns and trends in data.

Data Mining 52

Data Mining Business Intelligence BI Structured Data 52

Monte Carlo

AUGUST 25, 2023

Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Teams using a data warehouse usually leverage SQL queries for analytics use cases. This same structure aids in maintaining data quality and simplifies how users interact with and understand the data.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Knowledge Hut

JANUARY 30, 2024

Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52