ProjectPro

JUNE 6, 2025

ETL is a process that involves data extraction, transformation, and loading from multiple sources to a data warehouse, data lake, or another centralized data repository. An ETL developer designs, builds and manages data storage systems while ensuring they have important data for the business.

Data Engineer 59

Data Engineer Data Engineering Engineering ETL Tools 59

ProjectPro

JUNE 6, 2025

In addition to analytics and data science, RAPIDS focuses on everyday data preparation tasks. This features a familiar DataFrame API that connects with various machine learning algorithms to accelerate end-to-end pipelines without incurring the usual serialization overhead. However, Trino is not limited to HDFS access.

Big Data 49

Big Data Project Metadata Programming Language 49

ProjectPro

JUNE 6, 2025

This serverless data integration service can automatically and quickly discover structured or unstructured enterprise data when stored in data lakes in Amazon S3, data warehouses in Amazon Redshift, and other databases that are a component of the Amazon Relational Database Service.

AWS 66

AWS Scala Metadata Data Lake 66

ProjectPro

JUNE 6, 2025

They include relational databases like Amazon RDS for MySQL, PostgreSQL, and Oracle and NoSQL databases like Amazon DynamoDB. Types of AWS Databases AWS provides various database services, such as Relational Databases Non-Relational or NoSQL Databases Other Cloud Databases ( In-memory and Graph Databases).

AWS 40

AWS Database Amazon Web Services MySQL 40

ProjectPro

JUNE 6, 2025

Data Architect Salary How to Become a Data Architect - A 5-Step Guide Become a Data Architect - Key Takeaways FAQs on Data Architect Career Path What is a Data Architect Role? Cloud Architect stays up-to-date with data regulations, monitors data accessibility, and expands the cloud infrastructure as needed.

Data Architect 40

Data Architect Data Mining Programming Language Java 40

ProjectPro

JUNE 6, 2025

So, let’s dive into the list of the interview questions below - List of the Top Amazon Data Engineer Interview Questions Explore the following key questions to gauge your knowledge and proficiency in AWS Data Engineering. Become a Job-Ready Data Engineer with Complete Project-Based Data Engineering Course !

Data Engineer 40

Data Engineer Data Engineering Engineering NoSQL 40

ProjectPro

JUNE 6, 2025

Most of us have observed that data scientist is usually labeled the hottest job of the 21st century, but is it the only most desirable job? No, that is not the only job in the data world. Use machine learning algorithms to predict winning probabilities or player success in upcoming matches. venues or weather).

Data Engineer 59

Data Engineer Data Engineering Project Engineering 59

ProjectPro

JUNE 6, 2025

During peak hours, the pipeline handles around ~8 million events per second, with a data throughput reaching ~24 gigabytes per second. This data infrastructure forms the backbone for analytics, machine learning algorithms , and other critical systems that drive content recommendations, user personalization, and operational efficiency.

Data Engineer 40

Data Engineer Data Engineering Engineering NoSQL 40

ProjectPro

JUNE 6, 2025

Hive provides a high-level abstraction over Hadoop's MapReduce framework, enabling users to interact with data using familiar SQL syntax. This feature allows data analysts and developers to write hive queries in HQL, which is similar to SQL, making it easier for those familiar with relational databases to work with big data.

Big Data 40

Big Data Architecture Project Hadoop 40

ProjectPro

JUNE 6, 2025

A primary key is a column or set of columns in a relational database management system table that uniquely identifies each record. To avoid null values and duplicate entries, the primary key constraint is applied to the column data. List some of the benefits of data modeling. What is the definition of a primary key?

Data Warehouse 53

Data Warehouse NoSQL PostgreSQL Relational Database 53

ProjectPro

JUNE 6, 2025

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis.

Hadoop 40

Hadoop Project Big Data Scala 40

ProjectPro

JUNE 6, 2025

Read this blog to know more about the core AWS big data services essential for data engineering and their implementations for various purposes, such as big data engineering , machine learning, data analytics, etc. million organizations that want to be data-driven choose AWS as their cloud services partner.

AWS 40

AWS Data Engineer Data Engineering Engineering 40

Data Engineering Podcast

JUNE 10, 2018

Summary With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages.

PostgreSQL 100

PostgreSQL NoSQL Relational Database SQL 100

ProjectPro

JUNE 6, 2025

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 40

Big Data Hadoop Relational Database NoSQL 40

ProjectPro

JUNE 6, 2025

Unlike conventional databases confined to tabular structures, Vector Databases elevate data beyond mere entries; they transform into mathematical blueprints within a sprawling multi-dimensional space as vectors with each dimension capturing a unique attribute or feature. Looking for end to end solved machine learning projects?

Database 40

Database Algorithm Machine Learning Metadata 40

ProjectPro

JUNE 6, 2025

One of the most in-demand technical skills these days is analyzing large data sets, and Apache Spark and Python are two of the most widely used technologies to do this. Python is one of the most extensively used programming languages for Data Analysis, Machine Learning , and data science tasks.

Big Data 40

Big Data Data Process Process Kafka 40

ProjectPro

JUNE 6, 2025

With industries like finance, healthcare, and e-commerce increasingly relying on data-driven strategies, ETL engineers are crucial in managing vast data. Bureau of Labor Statistics projects a 22% growth rate for data engineers from 2020 to 2030, driven by the rise of big data, AI, and machine learning across various sectors.

Data Engineer 40

Data Engineer Data Engineering Engineering ETL Tools 40

ProjectPro

JUNE 6, 2025

Apache Spark Apache Spark is a powerful open-source framework for distributed data processing. It provides various libraries for batch processing, real-time streaming , machine learning, and graph processing. Spark's in-memory computing capabilities make it suitable for handling large-scale data transformations efficiently.

Data Pipeline 40

Data Pipeline Building Retail Data Ingestion 40

ProjectPro

JUNE 6, 2025

Table of Contents What are Big Data Tools? Why Are Big Data Tools Valuable to Data Professionals? Traditional data tools cannot handle this massive volume of complex data, so several unique Big Data software tools and architectural solutions have been developed to handle this task.

Big Data Tools 40

Big Data Tools Big Data Hadoop BI 40

Knowledge Hut

APRIL 25, 2024

Check out the Big Data courses online to develop a strong skill set while working with the most powerful Big Data tools and technologies. Look for a suitable big data technologies company online to launch your career in the field. What Are Big Data T echnologies? Let's explore the technologies available for big data.

Big Data 98

Big Data Technology NoSQL Hadoop 98

Data Engineering Weekly

JUNE 10, 2024

link] Open AI: Model Spec LLM models are slowly emerging as the intelligent data storage layer. Similar to how data modeling techniques emerged during the burst of relation databases, we started to see similar strategies for fine-tuning and prompt templates. Will they co-exist or fight with each other?

Data Engineer 103

Data Engineer Data Engineering Engineering Kafka 103

AltexSoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

JUNE 6, 2025

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis , data migration, data processing architecture, data storage, big data analytics, etc.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

Knowledge Hut

FEBRUARY 29, 2024

The designer must decide and understand the data storage, and inter-relation of data elements. Considering this information database model is fitted with data. It is created for the recovery and control of data in a relational database. SQL stands for Structured Query Language.

Data Science 98

Data Science Database Design Machine Learning Programming Language 98

ProjectPro

JUNE 6, 2025

Additional Costs Implementing and maintaining ETL pipelines can be costly, especially as data volumes grow, requiring significant infrastructure investment and ongoing maintenance. This helps organizations to streamline their operations directly assessing Salesforce data in Snowflake for analysis and decision-making.

Data Analytics 40

Data Analytics MySQL PostgreSQL Data Lake 40

ProjectPro

JUNE 6, 2025

Increased Efficiency: Cloud data warehouses frequently split the workload among multiple servers. As a result, these servers handle massive volumes of data rapidly and effectively. Handle Big Data: Storage in cloud-based data warehouses may increase independently of computational resources. What is Data Purging?

Data Warehouse 40

Data Warehouse Data Mining Recruitment Database 40

ProjectPro

JUNE 6, 2025

There are many cloud computing job roles like Cloud Consultant, Cloud reliability engineer, cloud security engineer, cloud infrastructure engineer, cloud architect, data science engineer that one can make a career transition to. PaaS packages the platform for development and testing along with data, storage, and computing capability.

Cloud Computing 53

Cloud Computing Cloud Amazon Web Services AWS 53

Striim

SEPTEMBER 11, 2024

Data Pipeline Use Cases Data pipelines are integral to virtually every industry today, serving a wide range of functions from straightforward data transfers to complex transformations required for advanced machine learning applications. Data storage Data storage follows.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Data Engineering Podcast

APRIL 22, 2019

Summary One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems.

Database 100

Database Cloud NoSQL Scala 100

ProjectPro

JUNE 6, 2025

The data integration aspect of the project is highlighted in the utilization of relational databases, specifically PostgreSQL and MySQL , hosted on AWS RDS (Relational Database Service). You will use Python libraries for data processing and transformation.

Data Integration 40

Data Integration Project Data Lake Hospitality 40

AltexSoft

JUNE 7, 2021

Master Nodes control and coordinate two key functions of Hadoop: data storage and parallel processing of data. Worker or Slave Nodes are the majority of nodes used to store data and run computations according to instructions from a master node. Data storage options. Data management and monitoring options.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

ProjectPro

JUNE 6, 2025

You can leverage your data stored in Amazon S3 with other AWS services for analytics, machine learning, and further processing. Data Migration Project to Migrate and Sync Data Between Two Cloud Platforms in Real-time. Therefore, this is another beneficial data migration use case scenario worth exploring.

Project 40

Project Google Cloud AWS MongoDB 40

ProjectPro

JUNE 6, 2025

AWS Cloud provides a wide range of on-demand solutions for data storage and movement, allowing companies to scale instantly and pay only for resources they use. Caching the information in the database improves the performance of the architecture.

AWS 40

AWS Architecture Amazon Web Services Cloud Computing 40

ProjectPro

JUNE 6, 2025

Spark saves data in memory (RAM), making data retrieval quicker and faster when needed. Spark is a low-latency computation platform because it offers in-memory data storage and caching. Additional libraries on top of Spark Core enable a variety of SQL, streaming, and machine learning applications.

Hadoop 68

Hadoop Metadata Java Datasets 68

DareData

JANUARY 30, 2023

Learn the most important data engineering concepts that data scientists should be aware of. As the field of data science and machine learning continues to evolve, it is increasingly evident that data engineering cannot be separated from it. Examples of NoSQL databases include MongoDB or Cassandra.

Data Engineer 52

Data Engineer Data Engineering Engineering NoSQL 52

ProjectPro

JUNE 6, 2025

Knowledge of the definition and architecture of AWS Big Data services and their function in the data engineering lifecycle, including data collection and ingestion, data analytics, data storage, data warehousing, data processing, and data visualization. big data and ETL tools, etc.

Certification 40

Certification Data Engineer Data Engineering Engineering 40

ProjectPro

JUNE 6, 2025

The ETL (Extract, Transform, Load) process follows four main steps: i) Connect and Collect: Connect to the data source/s and move data to local and crowdsource data storage. ii) Data transformation using computing services such as HDInsight, Hadoop , Spark, etc. What is an Azure SQL database?

Data Lake 60

Data Lake Metadata SQL Datasets 60