ProjectPro

JUNE 6, 2025

Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., They enable the connection of various data sources to the Hadoop environment.

ETL Tools

ProjectPro

JUNE 6, 2025

What are DBT macros, and how do they enhance SQL functionality in DBT? DBT (Data Build Tool) macros are reusable pieces of SQL code written in Jinja – a templating language that enhances SQL's functionality by enabling dynamic and modular code creation. How can DBT be used to handle incremental data loads?

Raw Data

ProjectPro

JUNE 6, 2025

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Trino is a distributed SQL query engine. Trino Source: trino.io

Big Data

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

ProjectPro

JUNE 6, 2025

Additional libraries on top of Spark Core enable a variety of SQL, streaming, and machine learning applications. Spark can integrate with Apache Cassandra to process data stored in this NoSQL database. Spark can connect to relational databases using JDBC, allowing it to perform operations on SQL databases.

Hadoop

ProjectPro

JUNE 6, 2025

Table of Contents What are Data Warehousing Tools? Why Choose a Data Warehousing Tool? Scalability to meet evolving data demands. Standard SQL support for querying. Flexible pricing options with encryption and data controls. Loading data can be time-consuming, especially for large volumes.

Data Storage

ProjectPro

JUNE 6, 2025

PySpark SQL and Dataframes A dataframe is a shared collection of organized or semi-structured data in PySpark. This collection of data is kept in Dataframe in rows with named columns, similar to relational database tables. With PySparkSQL, we can also use SQL queries to perform data extraction.

Big Data

ProjectPro

JUNE 6, 2025

Since data needs to be accessible easily, organizations use Amazon Redshift as it offers seamless integration with business intelligence tools and helps you train and deploy machine learning models using SQL commands. Amazon Redshift is helping over 10000 customers with its unique features and data analytics properties.

Data Pipeline

ProjectPro

JUNE 6, 2025

Here's how you can do it: Next, you need to learn how to of load data elements of structured data into DataFrames from various data sources in PySpark using pyspark sql import functions. It is conceptually similar to a table in a relational database or a pandas DataFrame in Python.

Big Data

ProjectPro

JUNE 6, 2025

These databases are completely managed by AWS, relieving users of time-consuming activities like server provisioning, patching, and backup. The relational databases- Amazon Aurora , Amazon Redshift, and Amazon RDS use SQL (Structured Query Language) to work on data saved in tabular formats.

Amazon Web Services

ProjectPro

JUNE 6, 2025

Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data warehouses store highly transformed, structured data that is preprocessed and designed to serve a specific purpose. Data from data warehouses is queried using SQL.

Data Lake

ProjectPro

JUNE 6, 2025

Data transformation is a crucial task since it greatly enhances the usefulness and accessibility of data. Load - Engineers can load data to the desired location, often a relational database management system (RDBMS), a data warehouse, or Hadoop, once it becomes meaningful.

Data Science

ProjectPro

JUNE 6, 2025

Let’s say you want to pull data from an API, clean it, and load it into an SQL database or data warehouse like PostgreSQL, BigQuery , or even a local CSV file. You’d rather write it once and let the data pipeline handle it. You don’t want to do this manually every day, right?

Python

ProjectPro

JUNE 6, 2025

Questions span data warehousing , ETL processes, big data technologies , SQL, data processing, optimization, security, privacy, and data visualization. The on-site assessments cover SQL , analytics, machine learning , and algorithms. How would you optimize a SQL query for a large dataset in a data warehouse?

Data Engineer

ProjectPro

JUNE 6, 2025

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 Engineer

ProjectPro

JUNE 6, 2025

Importance of Choosing the Correct Snowflake Data Types Build Your First Snowflake Project with ProjectPro FAQ’s 6 Snowflake Datatypes Every Data Engineer Must Know Like other relational databases, there are many data types in Snowflake, including basic SQL data types, to cater to multiple data needs.

Bytes

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

ProjectPro

JUNE 6, 2025

Hive is a data warehousing and SQL-like query language system built on top of Hadoop. Hive provides a high-level abstraction over Hadoop's MapReduce framework, enabling users to interact with data using familiar SQL syntax. Users interact with Hive using Hive Query Language (HQL), a SQL-like language.

Big Data

ProjectPro

JUNE 6, 2025

Getting acquainted with MongoDB will give you insights into how non-relational databases can be used for advanced web applications, like the ones offered by traditional relational databases. The underlying model is the crucial conceptual difference between MongoDB and other SQL databases.

MongoDB

ProjectPro

JUNE 6, 2025

ETL Data Engineers work with different data formats, such as structured, semi-structured, and unstructured data, and ensure that pipelines are efficient, scalable, and optimized for performance. Clean, reformat, and aggregate data to ensure consistency and readiness for analysis.

Data Engineer

ProjectPro

JUNE 6, 2025

Top 15 Data Analysis Tools to Explore in 2025 | Trending Data Analytics Tools 1. Google Data Studio 10. Looker Data Analytics Tools Comparison Analyze Data Like a Pro with These Data Analysis Tools FAQs on Data Analysis Tools Data Analysis Tools- What are they? Power BI 4. Apache Spark 6.

Data Analysis Tools

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. Step 1- Automating the Lakehouse's data intake.

Data Pipeline

ProjectPro

JUNE 6, 2025

Data engineers leverage AWS Glue's capability to offer all features, from data extraction through transformation into a standard Schema. AWS Redshift Amazon Redshift offers petabytes of structured or semi-structured data storage as an ideal data warehouse option.

AWS

ProjectPro

JUNE 6, 2025

Transform unstructured data into structured data by fixing errors, redundancies, missing numbers, and other anomalies, eliminating unnecessary data, optimizing data systems, and finding relevant insights. to perform those tasks efficiently. that will play a significant role in your career.

NoSQL

ProjectPro

JUNE 6, 2025

create_engine (from sqlalchemy) used to create a connection to an SQLite database (or other databases) in a more flexible way than sqlite3, enabling easier integration with Pandas and SQL operations. def create_connection(dw_file): ''' create connection with data warehouse ''' try: conn = sqlite3.connect(dw_file)

Python

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

Storage, Processing, & Analytics Following data collection, the stored data undergoes a series of transformative processes to prepare it for analysis. Based on scalability, performance, and data structure, data is stored in suitable storage systems, such as relational databases, NoSQL databases, or data lakes.

Data Science

ProjectPro

JUNE 6, 2025

Let us compare traditional data warehousing and Hadoop-based BI solutions to better understand how using BI on Hadoop proves more effective than traditional data warehousing- Point Of Comparison Traditional Data Warehousing BI On Hadoop Solutions Data Storage Structured data in relational databases.

Hadoop

ProjectPro

JUNE 6, 2025

Moreover, anything you can do in SQL, you can do in Tableau. You can paste and refer your SQL queries to design anything in Tableau. Tableau's popularity stems from its ability to connect to various data sources. It can also extract information from unstructured data and convert it into structured data.

Data Science

ProjectPro

JUNE 6, 2025

This data can be analysed using big data analytics to maximise revenue and profits. Hive Project - Visualising Website Clickstream Data with Apache Hadoop 3. To this group, we add a storage account and move the raw data. Then we create and run an Azure data factory (ADF) pipelines.

Hadoop

ProjectPro

JUNE 6, 2025

7 Popular GCP ETL Tools You Must Explore in 2025 This section lists the topmost GCP ETL services/tools that will allow you to build effective data pipelines and workflows for your data engineering projects. Cloud SQL Cloud SQL is a completely managed relational database service for SQL Server, MySQL, and PostgreSQL.

ETL Tools

ProjectPro

AUGUST 2, 2025

Step 2: Database Management and Data Analysis Before you can build AI models, it's essential to know how to access, manage, and analyze data effectively. Start with SQL, the standard language for querying structured data in relational databases.

Engineering

Rockset

JULY 9, 2019

One of the main hindrances to getting value from our data is that we have to get data into a form that’s ready for analysis. Consider the hoops we have to jump through when working with semi-structured data, like JSON, in relational databases such as PostgreSQL and MySQL. It sounds simple, but it rarely is.

Relational Database

Snowflake

AUGUST 25, 2023

Code Llama models outperform Llama2 models by 11-30 percent-accuracy points on text-to-SQL tasks and come very close to GPT4 performance. SQL—the standard programming language of relational databases—was not included in these benchmarks. We tested their skills at SQL generation by using a few-shot prompt specified here.

Coding

AltexSoft

JUNE 7, 2021

MapReduce performs batch processing only and doesn’t fit time-sensitive data or real-time analytics jobs. Data engineers who previously worked only with relational database management systems and SQL queries need training to take advantage of Hadoop. Cassandra excels at streaming data analysis.

Big Data Tools

ProjectPro

MARCH 19, 2015

As data processing requirements grow exponentially, NoSQL is a dynamic and cloud friendly approach to dynamically process unstructured data with ease.IT professionals often debate the merits of SQL vs. NoSQL but with increasing business data management needs, NoSQL is becoming the new darling of the big data movement.

NoSQL

Data Engineering Podcast

DECEMBER 8, 2019

Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines.

Data Warehouse