ProjectPro

JUNE 6, 2025

From the fundamentals to advanced concepts, it covers everything from a step-by-step process of creating PySpark UDFs, demonstrating their seamless integration with SQL , and practical examples to solidify your understanding. As data grows in size and complexity, so does the need for tailored data processing solutions.

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ProjectPro

JUNE 6, 2025

Azure Databricks is a robust cloud-based analytics and data processing platform for various data-related tasks. Compute Management: Databricks allows you to manage the compute resources required for data processing tasks. This is also where data processing takes place.

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ProjectPro

JUNE 6, 2025

Think of the data integration process as building a giant library where all your data's scattered notebooks are organized into chapters. You define clear paths for data to flow, from extraction (gathering structured/unstructured data from different systems) to transformation (cleaning the raw data, processing the data, etc.)

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ProjectPro

JUNE 6, 2025

If you're looking to revolutionize your data processing and analysis, Python for ETL is the key to unlock the door. Check out this ultimate guide to explore the fascinating world of ETL with Python and discover why it's the top choice for modern data enthusiasts. Python ETL really empowers you to transform data like a pro.

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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.

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Snowflake

JULY 22, 2024

Additional processing capability with SQL, as well as Snowflake capabilities like Stored Procedures, Snowpark , and Streams and Tasks, help streamline operations. LTIMindtree’s PolarSled Accelerator helps migrate existing legacy systems, such as SAP, Teradata and Hadoop, to Snowflake.

Data 119

Data Government Cloud Hadoop 119

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. They are also accountable for communicating data trends. Let us now look at the three major roles of data engineers.

Data Engineering 98

Data Engineering Data Engineer Engineering Hadoop 98

DataKitchen

APRIL 13, 2021

Airflow — An open-source platform to programmatically author, schedule, and monitor data pipelines. Apache Oozie — An open-source workflow scheduler system to manage Apache Hadoop jobs. DBT (Data Build Tool) — A command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively.

Consulting 121

Consulting Machine Learning Data Science Data Pipeline 121

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

MAY 14, 2024

The “legacy” table formats The data landscape has evolved so quickly that table formats pioneered within the last 25 years are already achieving “legacy” status. It was designed to support high-volume data exchange and compatibility across different system versions, which is essential for streaming architectures such as Apache Kafka.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Knowledge Hut

NOVEMBER 2, 2023

These Azure data engineer projects provide a wonderful opportunity to enhance your data engineering skills, whether you are a beginner, an intermediate-level engineer, or an advanced practitioner. Who is Azure Data Engineer? Azure SQL Database, Azure Data Lake Storage). Azure SQL Database, Azure Data Lake Storage).

Data Engineering 52

Data Engineering Data Engineer Project Coding 52

ProjectPro

JUNE 6, 2025

This includes knowledge of data structures (such as stack, queue, tree, etc.), A Machine Learning professional needs to have a solid grasp on at least one programming language such as Python, C/C++, R, Java, Spark, Hadoop , etc. Machine Learning engineers are often required to collaborate with data engineers to build data workflows.

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Knowledge Hut

MAY 23, 2024

Users can also leverage it for generating interactive visualizations over data. It also comes with lots of automation techniques that qualify users to eliminate manual data workflows. It can analyze data in real-time and can perform cluster management. It is much faster than other analytic workload tools like Hadoop.

Data Science 52

Data Science MongoDB Programming Language Hadoop 52

Monte Carlo

JULY 27, 2023

Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.

Engineering 52

Engineering Healthcare Data Warehouse Machine Learning 52

Knowledge Hut

SEPTEMBER 29, 2023

Microsoft Data Engineer Certification is one such certification which is most sought after by professionals. By combining data from various structured and unstructured data systems into structures, Microsoft Azure Data Engineers will be able to create analytics solutions.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

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.

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Monte Carlo

NOVEMBER 14, 2023

But even as the modern data stack continues to evolve, Airflow maintains its title as a perennial data orchestration favorite—and for good reason. Prefect’s control panel also offers scheduling, automatic retries, and instant alerting, ensuring you always have a clear view of your data processes.

Pipeline-centric 40

Pipeline-centric Google Cloud Python Data Workflow 40

Knowledge Hut

MARCH 28, 2024

Salary (Average) $135,094 per year (Source: Talent.com) Top Companies Hiring Deloitte, IBM, Capgemini Certifications Microsoft Certified: Azure Solutions Architect Expert Job Role 3: Azure Big Data Engineer The focus of Azure Big Data Engineers is developing and implementing big data solutions with the use of the Microsoft Azure platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

AltexSoft

SEPTEMBER 21, 2023

The Elastic Stacks Elasticsearch is integral within analytics stacks, collaborating seamlessly with other tools developed by Elastic to manage the entire data workflow — from ingestion to visualization. Beats facilitate data movement from source to destination, which can be either Elasticsearch or Logstash, depending on the use case.

Engineering 52

Engineering NoSQL Programming Language Java 52

Monte Carlo

JUNE 14, 2023

5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

DECEMBER 21, 2021

This includes knowledge of data structures (such as stack, queue, tree, etc.), A Machine Learning professional needs to have a solid grasp on at least one programming language such as Python, C/C++, R, Java, Spark, Hadoop, etc. Machine Learning engineers are often required to collaborate with data engineers to build data workflows.

Machine Learning 52

Machine Learning Engineering Algorithm Data Science 52

ProjectPro

JUNE 6, 2025

In the big data industry, Hadoop has emerged as a popular framework for processing and analyzing large datasets, with its ability to handle massive amounts of structured and unstructured data. Table of Contents Why work on Apache Hadoop Projects? FAQs Why work on Apache Hadoop Projects?

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phData: Data Engineering

JANUARY 3, 2022

phData Cloud Foundation is dedicated to machine learning and data analytics, with prebuilt stacks for a range of analytical tools, including AWS EMR, Airflow, AWS Redshift, AWS DMS, Snowflake, Databricks, Cloudera Hadoop, and more. This helps drive requirements and determines the right validation at the right time for the data.

IT 52

IT AWS Software Engineer Software Engineering 52

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.

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AltexSoft

NOVEMBER 7, 2022

DevOps tasks — for example, creating scheduled backups and restoring data from them. Airflow is especially useful for orchestrating Big Data workflows. Airflow is not a data processing tool by itself but rather an instrument to manage multiple components of data processing. When Airflow won’t work.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

ProjectPro

JUNE 6, 2025

By converting Excel-based sales data into dynamic visualizations, this solution supports data-driven strategy and performance tracking for retail and e-commerce businesses. This project uses Python libraries such as Pandas for data processing and Dash with Plotly for dashboard development.

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