ProjectPro

JUNE 6, 2025

Before we dive further into the comparison between ETL developers and other data industry job titles, let us first understand what is an ETL developer, what are the necessary skills and responsibilities associated with the role, etc. Begin simply by loading a sample dataset from a Kaggle competition into a data warehouse as a starting point.

Data Engineer 59

Data Engineer Data Engineering Engineering ETL Tools 59

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

Data Lake 115

Data Lake Data Warehouse Cloud Unstructured Data 115

ProjectPro

JUNE 6, 2025

Learn to Interact with the DBMS Systems Many companies keep their data warehouses far from the stations where data can be accessed. The role of a data engineer is to use tools for interacting with the database management systems. for working on cloud data warehouses.

Data Engineering 58

Data Engineering Data Engineer Engineering Amazon Web Services 58

ProjectPro

JUNE 6, 2025

Decide the process of Data Extraction and transformation, either ELT or ETL (Our Next Blog) Transforming and cleaning data to improve data reliability and usage ability for other teams from Data Science or Data Analysis. Dealing With different data types like structured, semi-structured, and unstructured data.

Data Science 40

Data Science Data Engineering Data Engineer Engineering 40

ProjectPro

JUNE 6, 2025

Top ETL Business Use Cases for Streamlining Data Management Data Quality - ETL tools can be used for data cleansing, validation, enriching, and standardization before loading the data into a destination like a data lake or data warehouse.

BI 45

BI ETL Tools Retail Healthcare 45

ProjectPro

JUNE 6, 2025

Google BigQuery BigQuery is a fully-managed, serverless cloud data warehouse by Google. It facilitates business decisions using data with a scalable, multi-cloud analytics platform. Additionally, it has excellent machine learning and business intelligence capabilities.

Data Engineer 40

Data Engineer Data Engineering Engineering Google Cloud 40

Data Engineering Podcast

JUNE 26, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Unstruk is the DataOps platform for your unstructured data.

Datasets 130

Datasets Unstructured Data Metadata MongoDB 130

Sync Computing

NOVEMBER 7, 2024

Data volume and velocity, governance, structure, and regulatory requirements have all evolved and continue to. Despite these limitations, data warehouses, introduced in the late 1980s based on ideas developed even earlier, remain in widespread use today for certain business intelligence and data analysis applications.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Data Engineering Podcast

JUNE 19, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Unstruk is the DataOps platform for your unstructured data.

Metadata 130

Metadata Unstructured Data MongoDB MySQL 130

Knowledge Hut

APRIL 23, 2024

Two popular approaches that have emerged in recent years are data warehouse and big data. While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Data Ingestion 52

ProjectPro

JUNE 6, 2025

Amazon, which generates massive volumes of data daily, faced this exact challenge. Extracting, transforming, and loading (ETL) data from their transactional databases into data warehouses like Redshift slowed their analytics, delaying crucial business decisions.

Data Analytics 40

Data Analytics MySQL PostgreSQL Data Lake 40

Knowledge Hut

MAY 31, 2023

Business Intelligence (BI) comprises a career field that supports organizations to make driven decisions by offering valuable insights. Business Intelligence is closely knitted to the field of data science since it leverages information acquired through large data sets to deliver insightful reports.

Business Intelligence 52

Business Intelligence BI Business Analyst Consulting 52

ProjectPro

JUNE 6, 2025

It can also access structured and unstructured data from various sources. As a result, it must combine with other cloud-based data platforms, if not HDFS. Spark Projects for Data Engineers Learn to Write Spark Applications using Spark 2.0 Apache Hive Apache Hive is a Hadoop-based data warehouse and management tool.

Data Engineering 45

Data Engineering Data Engineer Engineering Kafka 45

ProjectPro

JUNE 6, 2025

Ready to take your big data analysis to the next level? Check out this comprehensive tutorial on Business Intelligence on Hadoop and unlock the full potential of your data! million terabytes of data are generated daily. Table of Contents Why Business Intelligence On Hadoop?

BI 40

BI Hadoop Big Data Business Intelligence 40

ProjectPro

JUNE 6, 2025

A data mart is a cost-effective solution to efficiently acquire meaningful insights since it only comprises data relevant to a specific business area. It's a top-down method that starts with storing all enterprise data in a single area, then extracting a well-defined piece of the data when it's time to analyze it.

Data Warehouse 53

Data Warehouse NoSQL PostgreSQL Relational Database 53

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

ProjectPro

FEBRUARY 16, 2023

Business Intelligence and Artificial Intelligence are popular technologies that help organizations turn raw data into actionable insights. While both BI and AI provide data-driven insights, they differ in how they help businesses gain a competitive edge in the data-driven marketplace.

Business Intelligence 52

Business Intelligence BI Data Mining Raw Data 52

Data Engineering Weekly

JULY 21, 2024

Focus on Needs Over Nomenclature : Define the outcomes you want from your analytics team instead of getting caught up in the semantics of terms like analytics, data science, and business intelligence. The Three C’s of Analytics : Emphasize data creation, curation, and consumption.

Data Engineer 117

Data Engineer Data Engineering Engineering Generalist 117

ProjectPro

JUNE 6, 2025

Check out this ultimate guide to explore the fascinating world of ETL with Python and discover why it's the top choice for modern data enthusiasts. Python ETL really empowers you to transform data like a pro. ETL, which stands for Extract, Transform, Load, is a crucial process in data integration and data warehousing.

Python 40

Python ETL Tools Data Warehouse Programming Language 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

Data Engineering Podcast

JUNE 12, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Unstruk is the DataOps platform for your unstructured data.

Metadata 100

Metadata Unstructured Data MongoDB Business Intelligence 100

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 Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Data Engineering Podcast

NOVEMBER 27, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. images, documents, etc.)

Data Process 100

Data Process Process Metadata Data Lake 100

ProjectPro

JUNE 6, 2025

Transform- Data is validated, cleaned, and modified in the staging area so that you can integrate it with a target system. Load- Finally, you can store the data in a data warehouse or other storage systems. ELT involves three core stages- Extract- Importing data from the source server is the initial stage in this process.

Data Engineer 40

Data Engineer Data Engineering Engineering Data Lake 40

ProjectPro

JUNE 6, 2025

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

Bytes 40

Bytes Google Cloud Data Warehouse Cloud Storage 40

ProjectPro

JUNE 6, 2025

An ETL (Extract, Transform, Load) Data Engineer is responsible for designing, building, and maintaining the systems that extract data from various sources, transform it into a format suitable for data analysis, and load it into data warehouses, lakes, or other data storage systems.

Data Engineering 40

Data Engineering Data Engineer Engineering ETL Tools 40

ProjectPro

JUNE 6, 2025

It is popular for its versatility and ease of use, making it suitable for batch and streaming data ingestion scenarios. Learn more about how NiFi helps ingest real-time data efficiently by working on this Real-Time Streaming of Twitter Sentiments AWS EC2 NiFi Project.

Data Ingestion 40

Data Ingestion Kafka Google Cloud AWS 40

ProjectPro

JUNE 6, 2015

In an era of digital transformation of enterprises, there are several questions that have arisen- How can business intelligence provide real time insights? How can business intelligence scale and analyse the growing data heap? How can business intelligence meet changing business needs?

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Marts There is a thin line between Data Warehouses and Data Marts.

Data Engineer 98

Data Engineer Data Engineering Engineering Data Lake 98

ProjectPro

JUNE 6, 2025

Designing and managing data flows to support analytical initiatives is the core responsibility of a data engineer. The main challenge is creating a flow that merges data from multiple sources into a data warehouse or shared location. Both stream and batch real-time processing are supported.

Big Data Tools 40

Big Data Tools Big Data Hadoop Kafka 40

Data Engineering Podcast

JULY 31, 2022

Summary Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. What is involved in integrating Manta with an organization’s data systems?

IT 100

IT Metadata MongoDB MySQL 100

ProjectPro

JUNE 6, 2025

Data engineering courses also teach data engineers how to leverage cloud resources for scalable data solutions while optimizing costs. Suppose a cloud data engineer completes a course that covers Google Cloud BigQuery and its cost-effective pricing model.

Data Engineering 45

Data Engineering Data Engineer Cloud Engineering 45

Knowledge Hut

JANUARY 18, 2024

Roles and Responsibilities Finding data sources and automating the data collection process Discovering patterns and trends by analyzing information Performing data pre-processing on both structured and unstructured data Creating predictive models and machine-learning algorithms Average Salary: USD 81,361 (1-3 years) / INR 10,00,000 per annum 3.

Data Science 98

Data Science Data Architect Business Analyst ETL Method 98

ProjectPro

JUNE 6, 2025

FAQs on the Data Science Pipeline What is a Data Science Pipeline? A data science pipeline is a structured process that involves gathering raw and unstructured data from multiple sources, processing it through transformations like filtering and aggregating, and storing it in a data warehouse for analysis.

Data Science 40

Data Science Building AWS Data Lake 40

Towards Data Science

JULY 21, 2023

In this article, we’ll present you with the Five Layer Data Stack — a model for platform development consisting of five critical tools that will not only allow you to maximize impact but empower you to grow with the needs of your organization. Before you can model the data for your stakeholders, you need a place to collect and store it.

Building 98

Building Business Intelligence BI Cloud Storage 98

Monte Carlo

JULY 19, 2023

In this article, we’ll present you with the Five Layer Data Stack—a model for platform development consisting of five critical tools that will not only allow you to maximize impact but empower you to grow with the needs of your organization. Before you can model the data for your stakeholders, you need a place to collect and store it.

Building 96

Building Business Intelligence Cloud Storage BI 96