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 40

Big Data Data Process Process Kafka 40

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 Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

Here's an example of a job description of an ETL Data Engineer below: Source: www.tealhq.com/resume-example/etl-data-engineer Key Responsibilities of an ETL Data Engineer Extract raw data from various sources while ensuring minimal impact on source system performance.

Data Engineering 40

Data Engineering Data Engineer Engineering ETL Tools 40

DareData

JANUARY 30, 2023

Examples of relational databases include MySQL or Microsoft SQL Server. NoSQL databases: NoSQL databases are often used for applications that require high scalability and performance, such as real-time web applications. Examples of NoSQL databases include MongoDB or Cassandra.

Data Engineer 52

Data Engineer Data Engineering Engineering NoSQL 52

U-Next

SEPTEMBER 7, 2022

Data is stored in both a database and a data warehouse. These are systems for storing data. . As a general rule, the bottom tier of a data warehouse is a relational database system. A database is also a relational database system. The DW and databases support multi-user access.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

ProjectPro

JUNE 6, 2025

Image Credit: altexsoft.com Below are some essential components of the data pipeline architecture: Source: It is a location from where the pipeline extracts raw data. Data sources may include relational databases or data from SaaS (software-as-a-service) tools like Salesforce and HubSpot.

Data Pipeline 40

Data Pipeline Architecture Kafka Data Lake 40

ProjectPro

FEBRUARY 8, 2023

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 98

AWS Scala Metadata Data Lake 98

ProjectPro

OCTOBER 28, 2015

The major difference between Sqoop and Flume is that Sqoop is used for loading data from relational databases into HDFS while Flume is used to capture a stream of moving data. Table of Contents Hadoop ETL tools: Sqoop vs Flume-Comparison of the two Best Data Ingestion Tools What is Sqoop in Hadoop?

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

ProjectPro

JUNE 6, 2025

Expert Opinion On Why SQL Is Crucial For Data Analysts Shakra Shamim , Data Analyst at Myntra, shares her valuable opinion on why SQL is a key skill for becoming a data analyst- 1. Foundation of Databases: - Almost every business, irrespective of its size, relies on relational databases. -

Portfolio 40

Portfolio Programming Language Hadoop Consulting 40

Rockset

FEBRUARY 2, 2022

The benefit of these tools is that they’re built specifically for data analytics. They support joins and their column orientation allows you to quickly and effectively carry out aggregations. Data warehouses scale well and are well-suited to BI and advanced analytics use cases. Additionally, this approach doesn’t scale well.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

ProjectPro

JUNE 6, 2025

to accumulate data over a given period for better analysis. S3 is an object storage service provided by AWS that allows data to be stored and retrieved from anywhere on the web. The most recent CSV file in the S3 bucket is then downloaded and ingested into the Postgres data warehouse.

Data Engineering 59

Data Engineering Data Engineer Project Engineering 59

AltexSoft

AUGUST 4, 2021

Modern cloud warehouses make it possible to store data in its raw formats similarly to data lakes. A data mart is a subject-oriented relational database commonly containing a subset of DW data that is specific for a particular business department of an enterprise, e.g., a marketing department.

Data Lake 64

Data Lake Data Warehouse ETL Tools Telecommunication 64

Knowledge Hut

NOVEMBER 17, 2023

To be an Azure Data Engineer, you must have a working knowledge of SQL (Structured Query Language), which is used to extract and manipulate data from relational databases. You should be able to create intricate queries that use subqueries, join numerous tables, and aggregate data.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

AltexSoft

DECEMBER 23, 2022

All available data is pulled from a particular data source. This process can involve extracting all rows and columns of data from a relational database, all records from a file, or all data from an API endpoint. Partial data extraction with update notifications. Aggregation. Full extraction.

Process 52

Process Building Data Lake Raw Data 52

ProjectPro

NOVEMBER 15, 2021

DataFrames are used by Spark SQL to accommodate structured and semi-structured data. You can also access data through non-relational databases such as Apache Cassandra, Apache HBase, Apache Hive, and others like the Hadoop Distributed File System. However, Trino is not limited to HDFS access.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

JANUARY 25, 2022

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 52

Big Data Data Process Process Kafka 52

Rockset

SEPTEMBER 13, 2022

Further, data is king, and users want to be able to slice and dice aggregated data as needed to find insights. Users don't want to wait for data engineers to provision new indexes or build new ETL chains. They want unfettered access to the freshest data available.

SQL 52

SQL Database Relational Database NoSQL 52

AltexSoft

MARCH 14, 2023

Databases store key information that powers a company’s product, such as user data and product data. The ones that keep only relational data in a tabular format are called SQL or relational database management systems (RDBMSs).

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

JULY 27, 2021

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 Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

SEPTEMBER 21, 2023

Data in Elasticsearch is organized into documents, which are then categorized into indices for better search efficiency. Each document is a collection of fields, the basic data units to be searched. Fields in these documents are defined and governed by mappings akin to a schema in a relational database.

Engineering 52

Engineering NoSQL Java Programming Language 52

Knowledge Hut

SEPTEMBER 6, 2023

Skills acquired : Relational database concepts Retrieving data using the SQL SELECT statement. Sorting and restricting data. Using Conditional Expressions and Conversion functions Reporting Aggregated Data Using Group Functions Displaying data taken from multiple tables.

Certification 52

Certification Database MongoDB MySQL 52

ProjectPro

DECEMBER 7, 2021

Image Credit: altexsoft.com Below are some essential components of the data pipeline architecture: Source: It is a location from where the pipeline extracts raw data. Data sources may include relational databases or data from SaaS (software-as-a-service) tools like Salesforce and HubSpot.

Data Pipeline 52

Data Pipeline Architecture Kafka Data Lake 52

ProjectPro

JUNE 6, 2025

ETL is meant for extracting, transforming, and aggregating data. ETL is the first step in data warehousing. The data warehouse takes a long time to generate cross-tab reports from source tables. It just retrieves and manipulates databases. What is the difference between OLAP tools and ETL tools?

ETL Tools 40

ETL Tools Database-centric Data Warehouse ETL System 40

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 40

ETL Tools Big Data Google Cloud Project 40

ProjectPro

JUNE 6, 2025

The below code will show you how to perform feature engineering on aggregate data using Pandas- 4. You can use a relational database or a specialized feature store tool. In this example use case, you will use an SQLite database to store the features and feature definitions- 5.

Machine Learning 45

Machine Learning AWS Google Cloud Data Science 45

ProjectPro

JUNE 6, 2025

Hive also supports custom MapReduce scripts, making it a flexible and scalable solution for data processing and analytics in Hadoop. Hbase Apache HBase is a distributed, non-relational database built on top of Hadoop, providing fast and scalable storage for structured data. Repository Link: [link] 34.

Hadoop 40

Hadoop Project Big Data Media 40