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

ProjectPro

JUNE 6, 2025

They also enhance the data with customer demographics and product information from their databases. Data Storage Next, the processed data is stored in a permanent data store, such as the Hadoop Distributed File System (HDFS), for further analysis and reporting. Apache NiFi With over 4.1k

Data Ingestion

ProjectPro

JUNE 6, 2025

According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. Despite the buzz surrounding NoSQL , Hadoop , and other big data technologies, SQL remains the most dominant language for data operations among all tech companies.

Data Engineer

ProjectPro

JUNE 6, 2025

Parquet: Columnar storage format known for efficient compression and encoding, widely used in big data processing, especially in Apache Spark for data warehousing and analytics. Explain the difference between a Data Lake and a Data Warehouse. Are you a beginner looking for Hadoop projects?

Data Engineer

ProjectPro

JUNE 6, 2025

Data integration with ETL has evolved from structured data stores with high computing costs to natural state storage with read operation alterations thanks to the agility of the cloud. Data integration with ETL has changed in the last three decades. Q) What ETL does Amazon use? A) Amazon uses AWS Glue as its ETL tool.

AWS

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

ProjectPro

JUNE 6, 2025

Types of activities: Data Movement : Process of copying data from one data repository to another. Data Transformation : Refine data before transferring it to destination viz., HDInsight (Hive, Hadoop , Spark), Azure Functions, Azure Batch, Machine Learning, Data Lake Analytics.

Data Engineer

ProjectPro

JUNE 6, 2025

In 2024, the data engineering job market is flourishing, with roles like database administrators and architects projected to grow by 8% and salaries averaging $153,000 annually in the US (as per Glassdoor ). These trends underscore the growing demand and significance of data engineering in driving innovation across industries.

Data Engineer

ProjectPro

JUNE 6, 2025

DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Apache Spark is also quite versatile, and it can run on a standalone cluster mode or Hadoop YARN , EC2, Mesos, Kubernetes, etc. Trino is a distributed query tool for effectively querying large volumes of data.

Big Data

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

ProjectPro

JUNE 6, 2025

Their role includes designing data pipelines, integrating data from multiple sources, and setting up databases and data lakes that can support machine learning and analytics workloads. They work with various tools and frameworks, such as Apache Spark, Hadoop , and cloud services, to manage massive amounts of data.

Data Engineer

ProjectPro

JUNE 6, 2025

It also offers a unique architecture that allows users to quickly build tables and begin querying data without administrative or DBA involvement. Snowflake is a cloud-based data platform that provides excellent manageability regarding data warehousing, data lakes, data analytics, etc. What Does Snowflake Do?

Architecture

ProjectPro

JUNE 6, 2025

Before importing data into a data lake or data warehouse, AWS Glue is also responsible for conducting data transformation to the desired schema. Data engineers leverage AWS Glue's capability to offer all features, from data extraction through transformation into a standard Schema.

AWS

ProjectPro

JUNE 6, 2025

Data Processing: This is the final step in deploying a big data model. Typically, data processing is done using frameworks such as Hadoop, Spark, MapReduce, Flink , and Pig, to mention a few. How is Hadoop related to Big Data? Explain the difference between Hadoop and RDBMS. Define and describe FSCK.

Big Data

ProjectPro

JUNE 6, 2025

A good place to start would be to try the Snowflake Real Time Data Warehouse Project for Beginners from the ProjectPro repository. Worried about finding good Hadoop projects with Source Code ? ProjectPro has solved end-to-end Hadoop projects to help you kickstart your Big Data career.

Data Engineer

ProjectPro

JUNE 6, 2025

It is like a central location where quality data from multiple databases are stored. Data warehouses typically function based on OLAP (Online Analytical Processing) and contain structured and semi-structured data from transactional systems, operational databases, and other data sources.

Data Warehouse

ProjectPro

JUNE 6, 2025

Pandas Pandas is a popular Python data manipulation library often used for data extraction and transformation in ETL processes. It provides data structures and functions for working with structured data, making it an excellent choice for data preprocessing.

Python

ProjectPro

JUNE 6, 2025

Big data is often characterized by the seven V's: Volume , Variety , Velocity, Variability, Veracity, Visualization, and Value of data. Big data engineers leverage big data tools and technologies to process and engineer massive data sets or data stored in data storage systems like databases and data lakes.

Big Data

ProjectPro

JUNE 6, 2025

Learn from Industry Experts and Gain Job-Ready Skills with ProjectPro's GCP Data Engineering Training Lets understand this with a simple example of how a retailer can use BigQuery. Retail companies have huge amounts of data about customers, inventory, and sales that are stored across various sources databases, excel sheets, data lakes, etc.

Bytes

ProjectPro

JUNE 6, 2025

Mid-Level Big Data Engineer Salary Big Data Software Engineer's Salary at the mid-level with three to six years of experience is between $79K-$103K. Knowledge and experience in Big Data frameworks, such as Hadoop , Apache Spark , etc., As a result, there is a difference in the Big Data Engineer's salary by the skill-set.

Big Data

ProjectPro

JUNE 6, 2025

For example, a finance team could use Dataprep to validate financial data, such as bank statements and invoices, to ensure accuracy and prevent errors. Dataproc Google Cloud Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark jobs, Apache Flink, Presto, and over 30 other open-source tools and frameworks.

ETL Tools

ProjectPro

JUNE 6, 2025

Azure Table Storage- Azure Tables is a NoSQL database for storing structured data without a schema. It lets you store organized NoSQL data in the cloud and provides a schemaless key/attribute storage. Huge quantities of structured data are stored in the Windows Azure Table storage service.

BI

ProjectPro

JUNE 6, 2025

News on Hadoop-November 2016 Microsoft's Hadoop-friendly Azure Data Lake will be generally available in weeks. Microsoft's cloud-based Azure Data Lake will soon be available for big data analytic workloads. SQL component will allow users to query data. Source: [link] ) Hadoop for joy?

Hadoop

ProjectPro

JUNE 6, 2025

Web Server Log Processing In this project, you'll process web server logs using a combination of Hadoop, Flume, Spark, and Hive on Azure. Starting with setting up an Azure Virtual Machine, you'll install necessary big data tools and configure Flume agents for log data ingestion.

Project

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption.

Data Lake

Sync Computing

NOVEMBER 7, 2024

While data warehouses are still in use, they are limited in use-cases as they only support structured data. Data lakes add support for semi-structured and unstructured data, and data lakehouses add further flexibility with better governance in a true hybrid solution built from the ground-up.

Data Lake

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 Lake? .

Data Lake

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake

Data Engineering Podcast

FEBRUARY 9, 2020

What are some of the foundational skills and knowledge that are necessary for effective modeling of data warehouses? How has the era of data lakes, unstructured/semi-structured data, and non-relational storage engines impacted the state of the art in data modeling?

Data Lake

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

Data Lake

Data Engineering Podcast

AUGUST 31, 2020

Your host is Tobias Macey and today I’m interviewing Eldad Farkash about Firebolt, a cloud data warehouse optimized for speed and elasticity on structured and semi-structured data Interview Introduction How did you get involved in the area of data management?

Data Warehouse

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider.

Data Lake

Towards Data Science

FEBRUARY 15, 2023

Before going into further details on Delta Lake, we need to remember the concept of Data Lake, so let’s travel through some history. In theory, was just throwing everything inside Hadoop and later on writing jobs to process the data into the expected results, getting rid of complex data warehousing systems.

Data Lake

U-Next

MARCH 1, 2023

A solid understanding of relational databases and SQL language is a must-have skill, as an ability to manipulate large amounts of data effectively. A good Data Engineer will also have experience working with NoSQL solutions such as MongoDB or Cassandra, while knowledge of Hadoop or Spark would be beneficial.

Data Engineer

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. What is a data lake?

Data Lake

ProjectPro

JUNE 4, 2015

All the components of the Hadoop ecosystem, as explicit entities are evident. All the components of the Hadoop ecosystem, as explicit entities are evident. The holistic view of Hadoop architecture gives prominence to Hadoop common, Hadoop YARN, Hadoop Distributed File Systems (HDFS ) and Hadoop MapReduce of the Hadoop Ecosystem.

Hadoop

Towards Data Science

JANUARY 16, 2024

New data formats emerged — JSON, Avro, Parquet, XML etc. Result: Hadoop & NoSQL frameworks emerged. Data lakes were introduced to store the new data formats. Result: Cloud data warehouse offerings emerged as preferred solutions for relational and semi-structured data. So what was missing?

Data Warehouse