ProjectPro

JUNE 6, 2025

This article comprehensively looks at what is Apache Airflow and evaluates whether it's the right tool of choice for data engineers and data scientists. We know you are enthusiastic about building data pipelines from scratch using Airflow. How is Data Pipeline Flexibility Defined in Apache Airflow?

Data Pipeline

ProjectPro

JUNE 6, 2025

The process of data extraction from source systems, processing it for data transformation, and then putting it into a target data system is known as ETL, or Extract, Transform, and Load. ETL has typically been carried out utilizing data warehouses and on-premise ETL tools.

AWS

ProjectPro

JUNE 6, 2025

A survey by Data Warehousing Institute TDWI found that AWS Glue and Azure Data Factory are the most popular cloud ETL tools with 69% and 67% of the survey respondents mentioning that they have been using them. AWS Glue provides the functionality required by enterprises to build ETL pipelines.

AWS

ProjectPro

JUNE 6, 2025

Azure Data Factory is a cloud-based, fully managed, serverless ETL and data integration service offered by Microsoft Azure for automating data movement from its native place to, say, a data lake or data warehouse using ETL (extract-transform-load) OR extract-load-transform (ELT). Source: [link] 33.

Data Lake

ProjectPro

JUNE 6, 2025

A traditional ETL developer comes from a software engineering background and typically has deep knowledge of ETL tools like Informatica, IBM DataStage, SSIS, etc. He is an expert SQL user and is well in both database management and data modeling techniques. What does ETL Developer Do?

Data Engineer

ProjectPro

JUNE 6, 2025

Data engineers manage that massive amount of data using various data engineering tools, frameworks, and technologies. Data engineering tools are specialized applications that make building data pipelines and designing algorithms easier and more efficient.

Data Engineer

Monte Carlo

JULY 28, 2025

The platform integrates deeply with Snowflake features like streams and tasks, making it ideal for sophisticated data pipelines. It supports hybrid deployments and provides strong data quality features. Data + AI observability Data + AI observability ensures your Snowflake data remains accurate, fresh, and reliable.

BI

ProjectPro

JUNE 6, 2025

AWS Glue is a serverless platform that makes acquiring, managing, and integrating data for analytics, machine learning, and application development easier. It streamlines all data integration processes so that you can effectively and instantly utilize your integrated data.

AWS

ProjectPro

JUNE 6, 2025

Table of Contents Top 25 DBT Interview Questions and Answers DBT Interview Questions and Answers for Freshers DBT Interview Questions and Answers for Experienced Scenario-Based DBT Interview Questions and Answers Learn Building Data Pipelines Using DBT with ProjectPro!

Raw Data

ProjectPro

JUNE 6, 2025

This beginner's guide will give you a detailed overview of Azure Synapse Analytics and its architecture to help you build enterprise-grade data pipelines for your next data analytics project. Why Use Azure Synapse Analytics For Big Data Analytics Projects? Well, Azure Synapse Analytics is your answer!

Data Engineer

ProjectPro

JUNE 6, 2025

Source Code: Designing a Hadoop Architecture Apache Kafka Apache Kafka is a distributed streaming open-source framework used for large-scale data integration, real-time data pipelines, and stream processing. It quickly integrates and transforms cloud-based data.

Big Data Tools

ProjectPro

JUNE 6, 2025

Becoming a Big Data Engineer - The Next Steps Big Data Engineer - The Market Demand An organization’s data science capabilities require data warehousing and mining, modeling, data infrastructure, and metadata management. Most of these are performed by Data Engineers.

Big Data

ProjectPro

JUNE 6, 2025

Here are some key data modeling skills that every data modeller must possess- Understanding of Data Modelling Concepts and Techniques Data modelers must know how to use various data models to represent business concepts and workflows, relevant data elements and their relationships and characteristics, and technical metadata structures separately.

NoSQL

ProjectPro

JUNE 6, 2025

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process

Towards Data Science

APRIL 6, 2023

Today’s post follows the same philosophy: fitting local and cloud pieces together to build a data pipeline. And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them).

Data Pipeline

ProjectPro

OCTOBER 28, 2015

Some of the common challenges with data ingestion in Hadoop are parallel processing, data quality, machine data on a higher scale of several gigabytes per minute, multiple source ingestion, real-time ingestion and scalability. Need for Apache Sqoop How Apache Sqoop works? Need for Flume How Apache Flume works?

ETL Tools

Data Engineering Podcast

JANUARY 15, 2022

Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. You can observe your pipelines with built in metadata search and column level lineage.

Engineering

Towards Data Science

NOVEMBER 4, 2023

I’d like to discuss some popular Data engineering questions: Modern data engineering (DE). Does your DE work well enough to fuel advanced data pipelines and Business intelligence (BI)? Are your data pipelines efficient? PETL is great for aggregation and row-level ETL. What is it? Image by author.

Data Engineer

Cloudera

JUNE 16, 2022

CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. Reduce ingest latency and complexity: Multiple point solutions were needed to move data from different data sources to downstream systems. Meet Laila, a very opinionated practitioner of Cloudera Stream Processing.

Kafka

AltexSoft

OCTOBER 30, 2021

They’re integral specialists in data science projects and cooperate with data scientists by backing up their algorithms with solid data pipelines. Juxtaposing data scientist vs engineer tasks. One data scientist usually needs two or three data engineers. Managing data and metadata.

Data Engineer

ProjectPro

FEBRUARY 16, 2023

The process of data extraction from source systems, processing it for data transformation, and then putting it into a target data system is known as ETL, or Extract, Transform, and Load. ETL has typically been carried out utilizing data warehouses and on-premise ETL tools.

AWS

Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. However , to reduce the impact on the business, a data warehouse remains in use.

Data Lake

Data Engineering Weekly

DECEMBER 18, 2023

.” [link] Netflix: Our First Netflix Data Engineering Summit Netflix publishes the tech talk videos of their internal data summit. It is great to see an internal tech talk with a series focus on data engineering. My highlight is the talk about the data processing pattern around incremental data pipelines.

Data Engineer

Silectis

DECEMBER 15, 2020

Additionally, Magpie reduces your team’s IT complexity by eliminating the need to use separate data catalog, data exploration, and ETL tools. The whole data engineering process takes place directly within the platform, and eliminates the need to switch between different systems and tools. Or your team?

Data Engineer

ProjectPro

JANUARY 24, 2023

A survey by Data Warehousing Institute TDWI found that AWS Glue and Azure Data Factory are the most popular cloud ETL tools with 69% and 67% of the survey respondents mentioning that they have been using them. AWS Glue provides the functionality required by enterprises to build ETL pipelines.

AWS

Monte Carlo

JULY 22, 2024

While it shares similarities with software versioning, data versioning has unique characteristics specific to your data management needs. Maintaining metadata about each version. By implementing data versioning, you can create a systematic approach to managing the evolution of your data.

Metadata

ProjectPro

JANUARY 24, 2023

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. You can leverage AWS Glue to discover, transform, and prepare your data for analytics.

AWS

AltexSoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. However, there is a range of open-source client libraries enabling you to build Kafka data pipelines with practically any popular programming language or framework. ZooKeeper issue.

Kafka

Grouparoo

FEBRUARY 9, 2022

Today, organizations are adopting modern ETL tools and approaches to gain as many insights as possible from their data. However, to ensure the accuracy and reliability of such insights, effective ETL testing needs to be performed. So what is an ETL tester’s responsibility? Metadata testing.

Process

ProjectPro

JUNE 6, 2025

Data pipelines are crucial to the data infrastructure of any organization. Apache Airflow has recently garnered love from data engineers as a fantastic tool for managing their data extracts and transforms. It helps to manage any ETL (Extract, Transform, Load) operation and data pipelines.

MySQL

ProjectPro

JUNE 6, 2025

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. You can leverage AWS Glue to discover, transform, and prepare your data for analytics.

AWS

Monte Carlo

OCTOBER 5, 2023

effective communication that’s essential for coordinating ETL tasks, managing dependencies, and ensuring that everyone is aware of schedules, downtimes, and changes. increased vigilance in maintaining thorough documentation and metadata. Your data pipelines will thank you.

Data Cleanse

ProjectPro

JUNE 6, 2025

ETL is a crucial aspect of data management, and organizations want to ensure they're hiring the most skilled talent to handle their data pipeline needs. ETL is one of the most crucial elements in the design of the data warehousing architecture. The market for ETL tools is likely to grow at a CAGR of 13.9%

ETL Tools

Edureka

JULY 3, 2024

ADF connects to various data sources, including on-premises systems, cloud services, and SaaS applications. It then gathers and relocates information to a centralized hub in the cloud using the Copy Activity within data pipelines. Transform and Enhance the Data: Once centralized, data undergoes transformation and enrichment.

Pipeline-centric

Monte Carlo

SEPTEMBER 12, 2022

But with the rise of tools such as Segment, Fivetran, Meltano, and Airbyte, it’s become relatively easy for teams to bring all of their data from external sources into a centralized place like a data warehouse.

BI

dbt Developer Hub

NOVEMBER 3, 2022

We use Snowflake as our data warehouse where we build dashboards both for internal use and for customers. In the past we relied upon an ETL tool (Stitch) to pull data out of microservice databases and into Snowflake. This data would become the main dbt sources used by our report models in BI. Let's talk!

Kafka

ProjectPro

SEPTEMBER 26, 2021

Becoming a Big Data Engineer - The Next Steps Big Data Engineer - The Market Demand An organization’s data science capabilities require data warehousing and mining, modeling, data infrastructure, and metadata management. Most of these are performed by Data Engineers.

Big Data