PySpark vs Spark MySQL vs SQL ETL vs ELT Data Warehouse and Database Data mart vs Data Lake

Hello Connections,

Here is the list of concepts that I found confusing when I began my journey in the IT sector.

1. PySpark vs Spark

Spark is a generalpurpose distributed data processing engine that is suitable for use in a wide range of circumstances. On top of the Spark core data processing engine, there are libraries for SQL, machine learning, graph computation, and stream processing, which can be used together in an application.

PySpark is the Python library for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

Difference: PySpark is the Python API for Spark, allowing developers to use Python for Spark applications. On the other hand, Spark is an all-encompassing platform that supports multiple languages.

How are these 2 related: PySpark is just the Python library to use Spark's features. So, when you are using PySpark, you are actually using Spark. For example, you can use PySpark to write Spark applications using Python and perform big data processing operations.

2. MySQL vs SQL

SQL is a standard language for managing data held in a relational database management system (RDBMS).

MySQL is an RDBMS that allows keeping the data that exists in a database organized. It uses SQL to query the database.

Difference: SQL is a standard language that enables the user to design and manage databases like MySQL. MySQL, on the other hand, is a relational database management system that uses SQL to perform specific operations on the database.

How are these 2 related: MySQL is a database that uses the SQL language. For example, you can use SQL commands like SELECT, INSERT, UPDATE, DELETE, etc., in a MySQL database to manage your data.

3. ETL vs ELT

ETL (Extract, Transform, Load) is a process that involves extracting data from outside sources, transforming it to fit operational needs (which can include quality levels), then loading it into the end target database or data warehouse.

ELT (Extract, Load, Transform) is a variant of ETL wherein the extracted data is first loaded into the target system. Transformations are performed after the data is loaded into the target.

Difference: The key distinction between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) lies in the location and timing of the transformation process. ETL transforms data before it reaches the warehouse, while ELT does so afterward.

How are these 2 related: Both ETL and ELT are data integration processes used for data warehousing. The choice between ETL or ELT depends on the specific needs and architecture of your data environment. For example, you might use ETL if you need to enforce data quality, while ELT might be used when dealing with very large volumes of data where transformation in the target system is more efficient.

4. Data Warehouse vs Database

A Database is an organized collection of data stored and accessed electronically. It is designed to handle lots of transactions by many users.

Examples of Database: MySQL, Microsoft SQL Server.

A Data Warehouse is a large store of data collected from a wide range of sources used to guide business decisions. It is designed to support business decisions by allowing data consolidation, analysis, and reporting at different levels.

Examples of Data Warehouse: Snowflake, Amazon Redshift.

Difference: A database is designed to store data, whereas a Data warehouse is designed to analyze data. A database is an application-oriented collection of data, whereas Data Warehouse is a subject-oriented collection of data.

How are these 2 related: A data warehouse is a type of database that is optimized for reporting and analysis and is the cornerstone of Business Intelligence. For example, a company might use a transactional database to process orders, then run an ETL process to extract this transactional data and load it into a data warehouse for further analysis.

5. Data Mart vs Data Lake

A Data Mart is a subset of a data warehouse oriented to a specific business line or team. Whereas data warehouses have an enterprise wide depth, the information in data marts pertains to a single department.

Examples of Data Mart: Power BI, Azure Microsoft Data Factory.

A Data Lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data.

Examples of Data Lake: Amazon S3, Microsoft Azure Data Lake.

Difference: A data mart is a structured subset of data designed for a specific purpose, while a data lake is a storage system responsible for raw datasets.

How are these 2 related: Data marts and data lakes serve different purposes but can be used together in a data strategy. For example, a company might use a data lake to store all of its raw data and a data mart to store data that has been extracted, transformed, and loaded from the data lake for a specific department to use.