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. While all three are about data acquisition, they have distinct differences.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

SEPTEMBER 26, 2023

Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Whether your data is structured, like traditional relational databases, or unstructured, such as textual data, images, or log files, Azure Synapse can manage it effectively.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Cloudera

JUNE 16, 2022

CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. Faster data ingestion: streaming ingestion pipelines. Without context, streaming data is useless.”

Kafka 86

Kafka Manufacturing Data Lake SQL 86

Ascend.io

JANUARY 3, 2023

Here are some examples: The former United States Chief Data Scientist, DJ Patil, defined them in his book Data Jujitsu: The Art of Turning Data into Product as “a product that facilitates an end goal through the use of data”. Each widget on a dashboard can display a specific data product.

Data 52

Data Data Lake Business Intelligence Big Data 52

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis. Business Problem & Background.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

Rockset

JULY 7, 2021

Apache Druid is a real-time analytics database, providing business intelligence to drive clickstream analytics, analyze risk, monitor network performance, and more. This article focuses on implementing database joins in Apache Druid , looks at some limitations developers face, and explores possible solutions.

Database 40

Database Relational Database SQL MySQL 40

ProjectPro

JANUARY 31, 2023

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

MAY 14, 2021

And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. This data isn’t just about structured data that resides within relational databases as rows and columns. Big Data analytics processes and tools. Data ingestion.

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

SEPTEMBER 21, 2023

Data in Elasticsearch is organized into documents, which are then categorized into indices for better search efficiency. Each document is a collection of fields, the basic data units to be searched. Fields in these documents are defined and governed by mappings akin to a schema in a relational database.

Engineering 52

Engineering NoSQL Programming Language Java 52

AltexSoft

MARCH 14, 2023

Databases store key information that powers a company’s product, such as user data and product data. The ones that keep only relational data in a tabular format are called SQL or relational database management systems (RDBMSs). Data use component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

JULY 29, 2022

Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

DECEMBER 7, 2021

Image Credit: altexsoft.com Below are some essential components of the data pipeline architecture: Source: It is a location from where the pipeline extracts raw data. Data sources may include relational databases or data from SaaS (software-as-a-service) tools like Salesforce and HubSpot.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52