Monte Carlo

MAY 14, 2024

Apache ORC (Optimized Row Columnar) : In 2013, ORC was developed for the Hadoop ecosystem to improve the efficiency of data storage and retrieval.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Workfall

JUNE 27, 2023

It enables the collection of data from diverse platforms in real-time, organizing it into consolidated feeds while providing comprehensive metrics for monitoring. As a distributed data storage system, Kafka has been meticulously optimized to handle the continuous flow of streaming data generated by numerous sources.

Kafka 75

Kafka Architecture AWS Transportation 75

Precisely

MARCH 10, 2023

With the right geocoding technology, accurate and standardized address data is entirely possible. This capability opens the door to a wide array of data analytics applications. The Rise of Cloud Analytics Data analytics has advanced rapidly over the past decade.

Datasets 59

Datasets Data Warehouse Big Data Cloud Storage 59

Cloudera

SEPTEMBER 15, 2022

Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases. There are also newer AI/ML applications that need data storage, optimized for unstructured data using developer friendly paradigms like Python Boto API. Bucket types. release version.

Systems 86

Systems Hadoop Metadata Telecommunication 86

Cloudera

NOVEMBER 13, 2017

Cloud PaaS takes this a step further and allows users to focus directly on building data pipelines, training machine learning models, developing analytics applications — all the value creation efforts, vs the infrastructure operations.

Cloud 40

Cloud Big Data Metadata Machine Learning 40

AltexSoft

JULY 18, 2023

It has in-memory computing capabilities to deliver speed, a generalized execution model to support various applications, and Java, Scala, Python, and R APIs. Spark Streaming enhances the core engine of Apache Spark by providing near-real-time processing capabilities, which are essential for developing streaming analytics applications.

Big Data 52

Big Data Data Process Process Hadoop 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

SEPTEMBER 23, 2021

Harmonization of data includes numerous operations, such as data cleaning, indexing, mapping, formatting, providing semantic consistency, and many more. As the output, the data collected from various sources becomes consistent and readable for the end-point systems like analytics applications. Azure Data Factory.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

ProjectPro

MARCH 15, 2016

Hadoop is beginning to live up to its promise of being the backbone technology for Big Data storage and analytics. Companies across the globe have started to migrate their data into Hadoop to join the stalwarts who already adopted Hadoop a while ago. The solution to this problem is straightforward.

Hadoop 40

Hadoop Retail Healthcare Banking 40

ProjectPro

SEPTEMBER 6, 2021

Popular instances where GCP is used widely are machine learning analytics, application modernization, security, and business collaboration. It provides cloud storage and computing services across 93 availability zones and 29 geographic regions.

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. What are the main components of a big data architecture?

Big Data 52

Big Data Coding Project Hadoop 52

phData: Data Engineering

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. This noticeably saves time on copying and drastically reduces data storage costs.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52