ProjectPro

JUNE 6, 2025

Data warehousing tools are software applications designed to collect, store, manage, and analyze large volumes of data from various sources within an organization. These tools are crucial in modern business intelligence and data-driven decision-making processes.

Data Storage 58

Data Storage PostgreSQL Data Warehouse AWS 58

ProjectPro

JUNE 6, 2025

Here are some examples of the responsibilities handled by Data Engineers: Ingest data from different data sources (Based on the Business Use Case) Scheduling Data Received based on a pre-defined Data Collection Methodology. Maintain the data architecture over time and its scalability.

Data Science 40

Data Science Data Engineer Data Engineering Engineering 40

Edureka

APRIL 14, 2025

This blog breaks down how these tools complement and differ from one another to help you identify the best fit for your business. Understanding the Tools One platform is designed primarily for business intelligence, offering intuitive ways to connect to various data sources, build interactive dashboards, and share insights.

BI 40

BI Business Intelligence Raw Data Retail 40

Snowflake

NOVEMBER 2, 2023

And, since historically tools and commercial platforms were often designed to align with one specific architecture pattern, organizations struggled to adapt to changing business needs – which of course has implications on data architecture. The schema of semi-structured data tends to evolve over time.

Data Lake 115

Data Lake Data Warehouse Cloud Unstructured Data 115

ProjectPro

JUNE 6, 2025

Use AWS or third-party tools to analyze, re-architect, and re-platform on-premise data warehouses to data platforms in the AWS cloud. Using Java, Python, and Scala , design and construct production data pipelines from intake to consumption within a significant data architecture.

AWS 45

AWS Data Engineer Data Engineering Amazon Web Services 45

ProjectPro

JUNE 6, 2025

What does a Data Modeler do? The data modeler builds, implements, and analyzes data architecture and data modeling solutions using relational, dimensional, and NoSQL databases. As a data modeler, you must be familiar with data platform technologies and have hands-on big data platform and data warehouse expertise.

NoSQL 40

NoSQL ETL Tools Certification SQL 40

Striim

SEPTEMBER 11, 2024

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. Understanding the essential components of data pipelines is crucial for designing efficient and effective data architectures.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

ProjectPro

JUNE 6, 2025

AWS Data Engineer Interview Questions and Answers Explore AWS-focused questions and answers in this segment, encompassing data warehouse, Redshift, Glue, and overall cloud architecture, providing a comprehensive understanding of AWS services crucial for Amazon Data Engineering roles.

Data Engineer 40

Data Engineer Data Engineering Engineering NoSQL 40

ProjectPro

JUNE 6, 2025

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 40

Data Pipeline Architecture Kafka Data Lake 40

ProjectPro

JUNE 6, 2025

Big Data Engineer Salary by Skills The roles and responsibilities of a Big Data Engineer in an organization vary as per the business domain, type of the project, specific big data tools in use, IT infrastructure, technology stack, and a lot more. The compensation is higher than the other Software Engineers.

Big Data 40

Big Data Data Engineer Data Engineering Engineering 40

Knowledge Hut

OCTOBER 30, 2023

For any organization to grow, it requires business intelligence reports and data to offer insights to aid in decision-making. This data and reports are generated and developed by Power BI developers. A power BI developer has a crucial role in business management. Ensure compliance with data protection regulations.

BI 52

BI Data Cleanse Business Intelligence Business Analyst 52

Knowledge Hut

DECEMBER 26, 2023

Let us first get a clear understanding of why Data Science is important. What is the need for Data Science? If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets.

Data Engineer 98

Data Engineer Data Engineering Engineering Pipeline-centric 98

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 Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

ProjectPro

JUNE 6, 2025

Data Variety Hadoop stores structured, semi-structured and unstructured data. RDBMS stores structured data. Data storage Hadoop stores large data sets. RDBMS stores the average amount of data. Works with only structured data. Hardware Hadoop uses commodity hardware.

Big Data 40

Big Data Hadoop Relational Database AWS 40

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Traditional data warehouse platform architecture. Data lake architecture example.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard? What is a decentralized data architecture?

Data Lake 52

Data Lake Data Pipeline Data Warehouse Unstructured Data 52

Knowledge Hut

SEPTEMBER 26, 2023

This integration simplifies data access and management within the Azure cloud environment. Third-Party Integrations: Databricks offers connectors and integrations with popular third-party tools and services, including business intelligence (BI) platforms, data visualization tools, and machine learning frameworks.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

ProjectPro

JUNE 6, 2025

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

JANUARY 31, 2022

This is the reason why we need Data Warehouses. What is Snowflake Data Warehouse? A Data Warehouse is a central information repository that enables Data Analytics and Business Intelligence (BI) activities. The query processing layer is separated from the disk storage layer in the Snowflake data architecture.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Monte Carlo

JUNE 14, 2023

While it might be tempting to continue using custom code to transform your data, it does increase the chances of errors being made as the code is not easily replicable and must be rewritten every time a process takes place. Does data quality need to be high will directionally accurate suffice? Now Go Build Some Data Pipelines!

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

JULY 19, 2022

What data mesh is and is not. What data mesh IS. Data mesh is a set of principles for designing a modern distributed data architecture that focuses on business domains, not the technology used, and treats data as a product. So, to avoid any confusion, please be aware that data mesh is NOT.

Architecture 52

Architecture Data Lake Government Medical 52

phData: Data Engineering

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. It should also enable easy sharing of insights across the organization.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. This unchanging schema forms the foundation for all queries and business intelligence.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

AltexSoft

JULY 18, 2023

Spark SQL brings native support for SQL to Spark and streamlines the process of querying semistructured and structured data. It incorporates a comprehensive set of libraries, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

Big Data 52

Big Data Data Process Process Hadoop 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka Data Lake 52

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineer Salary by Skills The roles and responsibilities of a Big Data Engineer in an organization vary as per the business domain, type of the project, specific big data tools in use, IT infrastructure, technology stack, and a lot more. The compensation is higher than the other Software Engineers.

Big Data 52

Big Data Data Engineer Data Engineering Engineering 52

ProjectPro

JANUARY 31, 2023

Data Variety Hadoop stores structured, semi-structured and unstructured data. RDBMS stores structured data. Data storage Hadoop stores large data sets. RDBMS stores the average amount of data. Works with only structured data. Hardware Hadoop uses commodity hardware.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream data –Data Lakes fulfil a critical need. Data Warehouses do not retain all data whereas Data Lakes do.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

JULY 22, 2016

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream data –Data Lakes fulfil a critical need. Data Warehouses do not retain all data whereas Data Lakes do.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

OCTOBER 20, 2021

Data Integration at Scale Most data architectures rely on a single source of truth. Having multiple data integration routes helps optimize the operational as well as analytical use of data. We need to understand and monitor the current state of data evolution at the enterprise level.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

Edureka

MAY 28, 2025

It is a new way to store large amounts of data that takes the best parts of both data warehouses and data lakes and puts them together in one place. It lets you store all of your data in one place, including organized, semi-structured, and unstructured data. Need consistent, reliable reporting mechanisms.

Data Lake 40

Data Lake Data Warehouse Unstructured Data Business Intelligence 40

RandomTrees

MAY 6, 2025

This new technology is helping businesses make faster marketing predictions and better manage customer interactions. However, to succeed, AI requires a foundation of reliable and structured data. Modern data engineering can help with this. Without it, AI technologies wouldn’t have access to high-quality data.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Cleanse 52