Rockset

MARCH 19, 2020

Intro In recent years, Kafka has become synonymous with “streaming,” and with features like Kafka Streams, KSQL, joins, and integrations into sinks like Elasticsearch and Druid, there are more ways than ever to build a real-time analytics application around streaming data in Kafka.

Kafka 40

Kafka Database Process SQL 40

Cloudera

AUGUST 13, 2021

Every enterprise is trying to collect and analyze data to get better insights into their business. Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis.

Data Pipeline 107

Data Pipeline Data Lake ETL Tools Unstructured Data 107

Striim

AUGUST 31, 2023

This makes scaling the architecture complex and costly, as businesses will need to invest in additional hardware or cloud computing services in order to handle larger volumes of data processing. Finally, kappa architectures are not suitable for all types of data processing tasks.

Data Integration 69

Data Integration Architecture Amazon Web Services ETL System 69

Grouparoo

NOVEMBER 16, 2021

The critical benefit of transformation is that it allows analytical applications to efficiently access and process all data quickly and efficiently by eliminating issues before processing. An added benefit is that transformation to a standard format will make the manual inspection of data more convenient.

Data Mining 52

Data Mining Raw Data ETL Tools Unstructured Data 52

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 87

Systems Hadoop Metadata Telecommunication 87

AltexSoft

SEPTEMBER 23, 2021

A data hub, in turn, is rather a terminal or distribution station: It collects information only to harmonize it, and sends it to the required end-point systems. Data lake vs data hub. A data lake is quite opposite of a DW, as it stores large amounts of both structured and unstructured data.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Rockset

DECEMBER 9, 2019

It continuously ingests raw data from multiple sources--data lakes, data streams, databases--into its storage layer and allows fast SQL access from both visualisation tools and analytic applications. And if you are planning on copying huge amounts of data to Rockset, this also isn’t a problem.

Data Engineering 40

Data Engineering Data Engineer Raw Data Engineering 40

Cloudera

MAY 16, 2018

This example combines three types of unrelated data: Legal entity data: Two companies with completely unrelated business lines (coffee and waste management) merged together; Unstructured data: Fraudulent promotion campaigns took place through press releases and a fake stock-picking robot.

Data Lake 42

Data Lake Electronics Media Unstructured Data 42

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

MAY 23, 2015

Use market basket analysis to classify shopping trips Walmart Data Analyst Interview Questions Walmart Hadoop Interview Questions Walmart Data Scientist Interview Question American multinational retail giant Walmart collects 2.5 petabytes of unstructured data from 1 million customers every hour.

Big Data 52

Big Data Data Analysis Hadoop Retail 52

ProjectPro

MARCH 15, 2016

Such large commercial banks can leverage big data analytics more effectively by using frameworks like Hadoop on massive volumes of structured and unstructured data. Hadoop allows us to store data that we never stored before. Load a historical transactional point of sales data, into a Hadoop cluster.

Hadoop 40

Hadoop Retail Healthcare Banking 40

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. are examples of semi-structured data. How Big Data Works?

Big Data 52

Big Data Coding Project Hadoop 52

Cloudera

NOVEMBER 22, 2021

Open source frameworks such as Apache Impala, Apache Hive and Apache Spark offer a highly scalable programming model that is capable of processing massive volumes of structured and unstructured data by means of parallel execution on a large number of commodity computing nodes. .

Government 89

Government Hadoop Data Security Data Warehouse 89

U-Next

AUGUST 11, 2022

BI tools are different types of application software that collect and process huge amounts of unstructured data from internal and external sources. The enormous amounts of data being created provide a problem for firms of all kinds, making it tougher year after year to ensure that all business operations are under check.

Business Intelligence 40

Business Intelligence BI Unstructured Data Programming 40