Monte Carlo

OCTOBER 10, 2024

Let’s dive into the responsibilities, skills, challenges, and potential career paths for an AI Data Quality Analyst today. Table of Contents What Does an AI Data Quality Analyst Do? Handling unstructured data Many AI models are fed large amounts of unstructured data, making data quality management complex.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Monte Carlo

JANUARY 16, 2024

Data issues identified and resolved faster A bright and rapidly evolving future 1. Data lake and data warehouse convergence The data lake vs data warehouse question is constantly evolving. The maxim that data warehouses hold structured data while data lakes hold unstructured data is quickly breaking down.

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

Towards Data Science

DECEMBER 16, 2024

And over the last 24 months, an entire industry has evolved to service that very visionincluding companies like Tonic that generate synthetic structured data and Gretel that creates compliant data for regulated industries like finance and healthcare. But is synthetic data a long-term solution? Probablynot.

Unstructured Data 40

Unstructured Data Data Food Data Engineering 40

ProjectPro

JANUARY 27, 2023

Real-World ETL Use Cases and Applications Across Industries This blog discusses the numerous ETL use cases in various industries, including finance, healthcare, and retail. However, the vast volume of data will overwhelm you if you start looking at historical trends.

BI 52

BI ETL Tools Retail Healthcare 52

Knowledge Hut

NOVEMBER 3, 2023

MongoDB is a NoSQL database that’s been making rounds in the data science community. MongoDB’s unique architecture and features have secured it a place uniquely in data scientists’ toolboxes globally. Let us see where MongoDB for Data Science can help you.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

AltexSoft

JUNE 26, 2023

From the perspective of data science, all miscellaneous forms of data fall into three large groups: structured, semi-structured, and unstructured. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Knowledge Hut

JULY 28, 2023

Data Types and Dimensionality ML algorithms work well with structured and tabular data, where the number of features is relatively small. DL models excel at handling unstructured data such as images, audio, and text, where the data has a large number of features or high dimensionality.

Deep Learning 52

Deep Learning Machine Learning Unstructured Data Algorithm 52

Knowledge Hut

JULY 24, 2023

Disruptive database technologies include, for instance: Blockchain Blockchain is a distributed ledger technology that enables data to be safely stored across several nodes in a decentralized (or sometimes in a centralized) manner. In-memory Databases For applications that demand real-time data processing, in-memory databases are created.

Database 52

Database NoSQL Management Relational Database 52

Edureka

AUGUST 2, 2023

Data processing analysts are experts in data who have a special combination of technical abilities and subject-matter expertise. They are essential to the data lifecycle because they take unstructured data and turn it into something that can be used.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

AltexSoft

AUGUST 4, 2021

A data mart is a smaller subsection of a data warehouse built specifically for a particular subject area, business function, or group of users. The main idea is to provide a particular part of an organization with data that is the most relevant for their analytical needs.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

AltexSoft

AUGUST 29, 2023

Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources. Unstructured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

NOVEMBER 28, 2023

The data goes through various stages, such as cleansing, processing, warehousing, and some other processes, before the data scientists start analyzing the data they have garnered. The data analysis stage is important as the data scientists extract value and knowledge from the processed, structured data.

Data Science 52

Data Science Telecommunication Recruitment Finance 52

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 Relational Database AWS 52

Knowledge Hut

JUNE 19, 2023

Businesses use various data visualization techniques to present information from structured, semi-structured, or unstructured data collections. Adapting to business intelligence skills is necessary to survive in the rapidly growing technology industry.

Business Intelligence 52

Business Intelligence BI Programming Language Recruitment 52

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. In this role, they would help the Analytics team become ready to leverage both structured and unstructured data in their model creation processes. They construct pipelines to collect and transform data from many sources.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

ProjectPro

MARCH 15, 2016

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Let’s take a look at some Hadoop use cases in various industries. Hadoop in the Financial Sector The Finance sector is one of the major users of Hadoop.

Hadoop 40

Hadoop Retail Healthcare Banking 40

ProjectPro

OCTOBER 18, 2021

Students who are inclined to work in finance or fintech sectors must have this on their resume. There is a broad spectrum of data available on finance and the stock market. From the outset of machine learning, it was challenging to work with unstructured data (image dataset) and transform it into structured data (texts).

Machine Learning 52

Machine Learning Project Datasets Algorithm 52

ProjectPro

MARCH 17, 2021

Evolution of Machine Learning Applications in Finance : From Theory to Practice Let’s understand this with an example where you want the machine learning algorithm to categorize the images in the collection according to the two categories of fruits: Apples and Oranges.

Deep Learning 52

Deep Learning Machine Learning Algorithm Data Science 52

Striim

AUGUST 22, 2024

This layer leverages data integration platforms like Striim to connect to various data sources, ingest streaming data, and deliver it to various targets. Organizations seeking a mature, structured data solution that focuses on business intelligence and data analytics use cases may consider a data warehouse.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

ProjectPro

MAY 20, 2015

Several big data companies are looking to tame the zettabyte’s of BIG big data with analytics solutions that will help their customers turn it all in meaningful insights. Palantir Metropolis- This product focusses on information management, data integration and quantitative analytics.

Big Data 40

Big Data Hadoop Business Analyst Data Analytics 40

Knowledge Hut

JANUARY 11, 2024

I’ve observed that ever since data science techniques and approaches, including data science for finance, have made their way into different industrial domains, the growth has been tremendous. That is why companies are introducing data science into their operations and looking for people who can do efficient data analysis.

Finance 93

Finance Data Science Programming Language Machine Learning 93

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

Monte Carlo

JULY 19, 2023

The first layer of your stack will generally fall into one of three categories: a data warehouse solution like Snowflake that handles predominantly structured data; a data lake that focuses on larger volumes of unstructured data; and a hybrid solution like Databricks’ Lakehouse that combines elements of both.

Building 96

Building Business Intelligence Cloud Storage BI 96

Towards Data Science

JULY 21, 2023

The first layer of your stack will generally fall into one of three categories: a data warehouse solution like Snowflake that handles predominantly structured data; a data lake that focuses on larger volumes of unstructured data; and a hybrid solution like Databricks’ Lakehouse that combines elements of both.

Building 77

Building Business Intelligence BI Cloud Storage 77

Monte Carlo

JANUARY 27, 2024

The first layer of your stack will generally fall into one of three categories: a data warehouse solution like Snowflake that handles predominantly structured data; a data lake that focuses on larger volumes of unstructured data; and a hybrid solution like Databricks’ Lakehouse that combines elements of both.

Building 52

Building Business Intelligence Cloud Storage BI 52

ProjectPro

MARCH 14, 2014

The next decade of industries will be using Big Data to solve the unsolved data problems in the physical world. Big Data analysis will be about building systems around the data that is generated. Every department of an organization including marketing, finance and HR are now getting direct access to their own data.

Hadoop 40

Hadoop Big Data Data Mining Retail 40