Knowledge Hut

MARCH 15, 2024

Making decisions in the database space requires deciding between RDBMS (Relational Database Management System) and NoSQL, each of which has unique features. RDBMS uses SQL to organize data into structured tables, whereas NoSQL is more flexible and can handle a wider range of data types because of its dynamic schemas.

NoSQL 52

NoSQL Database-centric Relational Database PostgreSQL 52

Rockset

JULY 6, 2022

Similarly, databases are only useful for today’s real-time analytics if they can be both strict and flexible. Traditional databases, with their wholly-inflexible structures, are brittle. So are schemaless NoSQL databases, which capably ingest firehoses of data but are poor at extracting complex insights from that data.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Rockset

AUGUST 2, 2023

This enrichment data has changing schemas and new data providers are constantly being added to enhance the insights, making it challenging for Windward to support using relational databases with strict schemas. Windward also used specialized databases like Elasticsearch for specific functionality like text search.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52

Workfall

JULY 4, 2023

With DBT, they weave powerful SQL spells to create data models that capture the essence of their organization’s information. DBT’s superpowers include seamlessly connecting with databases and data warehouses, performing amazing transformations, and effortlessly managing dependencies to ensure high-quality data.

Data Warehouse 69

Data Warehouse SQL Database PostgreSQL 69

Striim

JANUARY 4, 2023

When your applications access data from each other, it can cause high coupling, i.e., applications are highly interdependent on each other. If you make any changes in the data structure, such as dropping a table from the database, it can affect the applications that are ingesting or using data from it.

PostgreSQL 52

PostgreSQL Data Warehouse Data Data Lake 52

Monte Carlo

FEBRUARY 15, 2023

On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems. What is a data warehouse? What’s the difference between a transactional database and a data warehouse?

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Knowledge Hut

JUNE 16, 2023

Roles and Responsibilities of a Data Analyst Data mining: Data analysts gather information from a variety of primary or secondary sources. They then arrange the data in a suitable format that is simple to understand. Upkeep of databases: Data analysts contribute to the design and upkeep of database systems.

Programming Language 52

Programming Language Data Analytics Data Science Cloud Computing 52

Data Engineering Weekly

FEBRUARY 12, 2023

– Postgres Innovation: Full-Text Search Any modern databases should support storing and processing semi-structured data & free text search. Expecting a well-defined upfront schema modeling is practically impossible with the variety of data sources we deal with.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Rockset

SEPTEMBER 20, 2022

Many of these services, as well as the dApps they may support, are built on transactional (OLTP) databases such as PostgreSQL, DynamoDB, MongoDB and others. These databases struggle to serve low latency results during high demand periods, when a lot of users are simultaneously querying data.

PostgreSQL 52

PostgreSQL MongoDB SQL Database 52

Silectis

JANUARY 3, 2021

Big Data Processing In order to extract value or insights out of big data, one must first process it using big data processing software or frameworks, such as Hadoop. Big Query Google’s cloud data warehouse. Cassandra A database built by the Apache Foundation. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Rockset

OCTOBER 20, 2021

Until now, the majority of the world’s data transformations have been performed on top of data warehouses, query engines, and other databases which are optimized for storing lots of data and querying them for analytics occasionally. For instance, let’s say you have streaming data coming in from Kafka or Kinesis.

SQL 52

SQL PostgreSQL MongoDB NoSQL 52

ProjectPro

SEPTEMBER 26, 2014

The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services. Big data, multi-structured data, and advanced analytics.

Business Intelligence 40

Business Intelligence Hadoop BI Relational Database 40

Edureka

JULY 16, 2024

Introduction Amazon Redshift, a cloud data warehouse service from Amazon Web Services (AWS), will directly query your structured and semi-structured data with SQL. To fully understand what makes AWS Redshift stand out, one only has to consider how the service performs data processing in petabytes.

AWS 40

AWS Data Warehouse Amazon Web Services Business Intelligence 40

ProjectPro

DECEMBER 10, 2021

For apps that depend exclusively on the search for both feature extraction and content navigation, Azure Cognitive Search is the ideal cloud provider for full-text search operations over content repositories and databases on Azure. It is a cloud-based NoSQL database that deals mainly with modern app development. What is Azure CosmosDB?

BI 52

BI Cloud Computing SQL Database 52

ProjectPro

JULY 27, 2021

Differentiate between relational and non-relational database management systems. Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language).

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

Knowledge Hut

JULY 24, 2023

In this digital age, data is king, and how we manage, analyze, and harness its power is constantly evolving. Database management, once confined to IT departments, has become a strategic cornerstone for businesses across industries. In this blog, we will talk about the future of database management.

Database 52

Database NoSQL Management Relational Database 52

Rockset

JULY 9, 2019

Consider the hoops we have to jump through when working with semi-structured data, like JSON, in relational databases such as PostgreSQL and MySQL. JSON is a good match for document databases, such as MongoDB. JSON is a good match for document databases, such as MongoDB.

Relational Database 40

Relational Database PostgreSQL MySQL Database 40

ProjectPro

FEBRUARY 16, 2023

If you are still wondering whether or why you need to master SQL for data engineering, read this blog to take a deep dive into the world of SQL for data engineering and how it can take your data engineering skills to the next level. Data engineers can perform any quality checks using the DDL commands in SQL.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Knowledge Hut

JANUARY 11, 2024

Data science is the field of study that deals with a huge volume of data using modern technologically driven tools and techniques to find some sort of pattern and derive meaningful information out of it that eventually helps in business and financial decisions. This work is done by financial data scientists.

Finance 93

Finance Data Science Programming Language Machine Learning 93

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

APRIL 26, 2016

Pig vs Hive Criteria Pig Hive Type of Data Apache Pig is usually used for semi structured data. Used for Structured Data Schema Schema is optional. Language It is a procedural data flow language. HBase is a NoSQL database. It is suggested to use standalone real database like PostGreSQL and MySQL.

Hadoop 40

Hadoop Metadata SQL Database 40