AltexSoft

OCTOBER 3, 2022

Bringing data together from heterogeneous enterprise sources and creating a unified view of that data is a popular application of knowledge graphs. This is especially useful for big enterprises when the sources are presented in different formats (CSV, XML, JSON, relational databases) and use different data schemas.

Relational Database 52

Relational Database Banking Media Computer Science 52

Knowledge Hut

DECEMBER 26, 2023

Analyzing and organizing raw data Raw data is unstructured data consisting of texts, images, audio, and videos such as PDFs and voice transcripts. The job of a data engineer is to develop models using machine learning to scan, label and organize this unstructured data.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Cloudera

SEPTEMBER 7, 2018

Whether it’s a rogue trader in a bank or brokerage or someone illegally sharing company intellectual property or intelligence, illegal insider actions put enterprises at risk of losing millions. This could be in the form of reputational damage or unfavorable regulatory consequences in case of compromised customer data, for example.

Machine Learning 49

Machine Learning Unstructured Data Portfolio Relational Database 49

ProjectPro

OCTOBER 28, 2015

Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., and Flume in Hadoop is used to sources data which is stored in various sources like and deals mostly with unstructured data. The complexity of the big data system increases with each data source.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

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. JP Morgan has massive amounts of data on what its customers spend and earn.

Hadoop 52

Hadoop Big Data Data Analytics Banking 52

Knowledge Hut

DECEMBER 26, 2023

Some of these ideas consist of: Big data technology and technologists deal with a number of similar problems, such as data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns. Relational and non-relational databases, such as RDBMS, NoSQL, and NewSQL databases.

Big Data 75

Big Data Data Mining Business Intelligence Machine Learning 75

AltexSoft

NOVEMBER 23, 2021

As a result, data virtualization enabled the company to conduct advanced analytics and data science, contributing to the growth of the business. Global investment bank: Cost reduction with more scalable and effective data management. Data virtualization architecture example. Connection layer.

Process 69

Process Data Lake Metadata Data Warehouse 69

ProjectPro

APRIL 1, 2016

eWeek.com Syncsort has made it easy for mainframe data to work in Hadoop and Spark by upgrading its DMX-h data integration software. Syncsort has delivered this because some of the companies in industries like financial services, banking, and insurance needed to maintain their mainframe data in native format.

Hadoop 52

Hadoop BI Big Data Big Data Tools 52

ProjectPro

NOVEMBER 14, 2015

These projects are proof of how far Apache Hadoop and Apache Spark have come and how they are making big data analysis a profitable enterprise. With Big Data came a need for programming languages and platforms that could provide fast computing and processing capabilities.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

MAY 31, 2021

However, there are ways to improve big data optimization- Reduce Processing Latency- Conventional database models have latency in processing because data retrieval takes a long time. Analyze Data Before Taking Actions- It's advisable to examine data before acting on it by combining batch and real-time processing.

Big Data 52

Big Data Coding Project Hadoop 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

SEPTEMBER 11, 2015

For professionals from BI background, learning Hadoop is necessary because with data explosion it is becoming difficult for traditional databases to store unstructured data. Hadoop still has a long way to go when it comes to presenting clean and readable data solutions. Hadoop is not suitable for all kinds of data.

Hadoop 52

Hadoop Java BI Big Data 52

U-Next

AUGUST 3, 2022

A high-ranking expert is known as a “Data Scientist” who works with big data and has the mathematics, economic, technical, analytic, and technological abilities necessary to cleanse, analyse and evaluate organised and unstructured data to help organisations make more informed decisions.

Retail 52

Retail Data Science Computer Science Entertainment 52