Start Data Engineering

AUGUST 17, 2021

Batch Data Pipelines 1.1 Process => Data Warehouse 1.2 Process => Cloud Storage => Data Warehouse 2. Near Real-Time Data pipelines 2.1 Data Stream => Consumer => Data Warehouse 2.2 Near Real-Time Data pipelines 2.1 Introduction Patterns 1.

Data Warehouse 130

Data Warehouse Cloud Storage Data Pipeline Data 130

Snowflake

MARCH 2, 2023

Snowflake enables organizations to be data-driven by offering an expansive set of features for creating performant, scalable, and reliable data pipelines that feed dashboards, machine learning models, and applications. But before data can be transformed and served or shared, it must be ingested from source systems.

Kafka 137

Kafka Data Ingestion Data Pipeline Cloud Storage 137

ProjectPro

JUNE 6, 2025

Azure Data Factory Before jumping right into comparing Azure Data Factory vs. Airflow, let us first understand the two tools. Your complex data pipelines may be simply scheduled and executed using the workflow engine Apache Airflow. These users can create data integration workflows without requiring specialized knowledge.

Cloud Storage 40

Cloud Storage Data Pipeline Big Data Data Integration 40

DataKitchen

NOVEMBER 5, 2024

This foundational layer is a repository for various data types, from transaction logs and sensor data to social media feeds and system logs. By storing data in its native state in cloud storage solutions such as AWS S3, Google Cloud Storage, or Azure ADLS, the Bronze layer preserves the full fidelity of the data.

Architecture 79

Architecture Raw Data Pipeline-centric Data Ingestion 79

ProjectPro

JUNE 6, 2025

Managing complex data pipelines can be challenging, requiring coordination between multiple systems and teams. Apache Airflow DAGs provide a powerful tool for creating and managing data pipelines, streamlining the process of data processing and automation. This is where Apache Airflow DAGs come in.

Data Pipeline 63

Data Pipeline PostgreSQL Python Database 63

Start Data Engineering

NOVEMBER 8, 2020

Introduction If you are looking for a simple, cheap data pipeline to pull small amounts of data from a stable API and store it in a cloud storage, then serverless functions are a good choice.

AWS 130

AWS Cloud Storage Data Pipeline Data 130

ProjectPro

JUNE 6, 2025

Storage And Persistence Layer Once processed, the data is stored in this layer. Stream processing engines often have in-memory storage for temporary data, while durable storage solutions like Apache Hadoop, Amazon S3, or Google Cloud Storage serve as repositories for long-term storage of processed data.

Data Ingestion 40

Data Ingestion Kafka Google Cloud AWS 40

ProjectPro

JUNE 6, 2025

You will download the Yelp dataset in JSON format for this project, connect it to the Cloud SDK by connecting to the Cloud storage, which is then connected to the Cloud Composer, and publish the Yelp dataset JSON stream to a PubSub topic. For this project, you will require the COVID-19 Cases.csv dataset from data.world.

Google Cloud 40

Google Cloud Cloud Cloud Storage Data Ingestion 40

ProjectPro

JUNE 6, 2025

Source- Building A Serverless Pipeline using AWS CDK and Lambda ETL Data Integration From GCP Cloud Storage Bucket To BigQuery This data integration project will take you on an exciting journey, focusing on extracting, transforming, and loading raw data stored in a Google Cloud Storage (GCS) bucket into BigQuery using Cloud Functions.

Data Integration 40

Data Integration Project Data Lake PostgreSQL 40

ProjectPro

JUNE 6, 2025

Amazon brought innovation in technology and enjoyed a massive head start compared to Google Cloud, Microsoft Azure , and other cloud computing services. It developed and optimized everything from cloud storage, computing, IaaS, and PaaS. AWS S3 and GCP Storage Amazon and Google both have their solution for cloud storage.

AWS 49

AWS Amazon Web Services Google Cloud Cloud Storage 49

ProjectPro

JUNE 6, 2025

Data Lake Architecture- Core Foundations Data lake architecture is often built on scalable storage platforms like Hadoop Distributed File System (HDFS) or cloud services like Amazon S3, Azure Data Lake, or Google Cloud Storage. FAQs on Building a Data Lake 1.

Data Lake 40

Data Lake Building Hadoop Raw Data 40

ProjectPro

JUNE 6, 2025

Want to process peta-byte scale data with real-time streaming ingestions rates, build 10 times faster data pipelines with 99.999% reliability, witness 20 x improvement in query performance compared to traditional data lakes, enter the world of Databricks Delta Lake now. What format does Delta lake use for storing data?

Data Lake 63

Data Lake Data Warehouse Metadata Unstructured Data 63

ProjectPro

JUNE 6, 2025

Real-Time Clickstream Analytics with Iceberg and Flink This project focuses on building a real-time analytics pipeline by ingesting clickstream data using Apache Flink and storing it in Iceberg table format on Google Cloud storage. Who owns Apache Iceberg? How does Apache Iceberg Compare to Snowflake?

Architecture 40

Architecture Data Lake Metadata Cloud Storage 40

Striim

MARCH 21, 2025

Striim customers often utilize a single streaming source for delivery into Kafka, Cloud Data Warehouses, and cloud storage, simultaneously and in real-time. Building streaming data pipelines shouldnt require custom coding Building data pipelines and working with streaming data should not require custom coding.

Process 52

Process Kafka Data Warehouse Data Pipeline 52

ProjectPro

JUNE 6, 2025

A professional data engineer designs systems to gather and navigate data. Data engineers require strong experience with multiple data storage technologies and frameworks to build data pipelines. Join the Best Data Engineering Course to Learn from Industry Leaders!

Data Engineering 40

Data Engineering Data Engineer Google Cloud Engineering 40

DataKitchen

DECEMBER 9, 2024

They opted for Snowflake, a cloud-native data platform ideal for SQL-based analysis. The team landed the data in a Data Lake implemented with cloud storage buckets and then loaded into Snowflake, enabling fast access and smooth integrations with analytical tools.

Pharmaceutical 65

Pharmaceutical Data Lake Cloud Storage Data Engineering 65

ProjectPro

JUNE 6, 2025

It downloads the Yelp dataset in JSON format, connects to Cloud SDK through Cloud storage, and connects to Cloud Composer. Cloud composer and PubSub outputs connect to Google Dataflow using Apache Beam. Lastly, Google Data Studio is used to visualize the data.

Data Warehouse 40

Data Warehouse Coding Project Google Cloud 40

ProjectPro

JUNE 6, 2025

To be more specific, ETL developers are responsible for the following tasks: Creating a Data Warehouse - ETL developers create a data warehouse specifically designed to meet the demands of a company after determining the needs. Data engineers are responsible for designing and maintaining data pipelines and infrastructures.

Data Engineering 59

Data Engineering Data Engineer Engineering ETL Tools 59

ProjectPro

JUNE 6, 2025

If you are willing to enter the big data industry and searching for some good Talend projects for resume, you must explore some of the unique Talend ETL projects in this blog. Talend is a popular open-source ETL tool for data integration. Additionally, it enables organizations to use data effectively and make decisions in real-time.

ETL Tools 40

ETL Tools Project MySQL Banking 40

Cloudera

DECEMBER 21, 2021

In working with thousands of customers deploying Spark applications, we saw significant challenges with managing Spark as well as automating, delivering, and optimizing secure data pipelines. We wanted to develop a service tailored to the data engineering practitioner built on top of a true enterprise hybrid data service platform.

Data Engineering 118

Data Engineering Data Engineer Engineering Data Pipeline 118

ProjectPro

JUNE 6, 2025

As businesses continue to recognize the value of efficient data management, the demand for certified data engineers has surged. These roles typically involve working with large-scale data solutions, implementing data pipelines, and optimizing data architectures for performance and scalability.

Data Engineering 40

Data Engineering Data Engineer Google Cloud Engineering 40

ProjectPro

JUNE 6, 2025

Using Vertex AI notebooks, the Dataflow interface enables the development and deployment of data pipelines based on the most recent data science and machine learning (ML) frameworks. GCP Dataflow SQL Dataflow SQL allows the utilization of SQL to develop streaming pipelines directly from the Google BigQuery web user interface.

Google Cloud 40

Google Cloud Java Data Ingestion SQL 40

ProjectPro

JUNE 6, 2025

Build, Design, and maintain data architectures using a systematic approach that satisfies business needs. Create high-grade data products by coordinating with engineering, product, data scientists , and business teams. Develop optimized data pipelines and make sure they are executed with high performance.

Data Engineering 49

Data Engineering Data Engineer Engineering Hadoop 49

ProjectPro

JUNE 6, 2025

These practices are crucial for building robust and scalable data pipelines, maintaining data quality, and enabling data-driven decision-making. Let us dive into some of the crucial best practices for data engineering that data engineers must implement in their data workflows and projects.

Data Workflow 40

Data Workflow Data Engineering Data Engineer Data Cleanse 40

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka Data Lake 52

Cloudera

MARCH 31, 2021

The combination of these capabilities will allow customers to easily migrate existing data pipelines to GCP or quickly set up new ones that can ingest from a number of existing or new data sources. Google Cloud Storage buckets – in the same subregion as your subnets .

Google Cloud 94

Google Cloud Cloud Amazon Web Services Cloud Storage 94

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). Checkout: [link] 48.

Data Lake 60

Data Lake Metadata SQL Datasets 60

ProjectPro

JUNE 6, 2025

The global data analytics market is expected to reach 68.09 Businesses are finding new methods to benefit from data. Data engineering entails building data pipelines for ingesting, modifying, supplying, and sharing data for analysis. Scalability ELT can be highly adaptable when using raw data.

Data Engineering 40

Data Engineering Data Engineer Engineering Data Lake 40

ProjectPro

JUNE 6, 2025

BigQuery enables users to store data in tables, allowing them to quickly and easily access their data. It supports structured and unstructured data, allowing users to work with various formats. BigQuery also supports many data sources, including Google Cloud Storage, Google Drive, and Sheets.

Bytes 40

Bytes Google Cloud Data Warehouse Cloud Storage 40

Monte Carlo

JULY 19, 2023

Like bean dip and ogres , layers are the building blocks of the modern data stack. Its powerful selection of tooling components combine to create a single synchronized and extensible data platform with each layer serving a unique function of the data pipeline. Let’s dive into it. The content, not the bean dip.

Building 96

Building Business Intelligence Cloud Storage BI 96

ProjectPro

JUNE 6, 2025

Gaining familiarity with these cloud environments will benefit anyone who wants to learn Snowflake Datawarehouse. For instance, you can retrieve data from an existing table- Data Loading You must begin by grasping the fundamentals of data loading. Snowflake supports loading data from cloud storage (e.g.,

Data Warehouse 40

Data Warehouse SQL AWS Big Data 40

ProjectPro

JUNE 6, 2025

However, creating and deploying these agents often involves challenges such as managing complex data workflows, integrating machine learning models, and ensuring scalability across operations. It handles unstructured data, integrates external APIs, and manages prompt engineering workflows. Python-based library integrating LLMs (e.g.,

Building 40

Building Data Workflow Python Data Pipeline 40

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? and parallel data processing. What is it? ML model training using Airflow.

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

ProjectPro

JUNE 6, 2025

Python for ETL enables data engineers and analysts to automate and manage data pipelines, apply transformations, and handle data integration efficiently. It is a popular choice in data engineering and data analytics. ETL can enhance data by adding information from external sources.

Python 40

Python ETL Tools Data Warehouse Programming Language 40

ProjectPro

JUNE 6, 2025

AWS is well-suited for hosting static websites, offering scalable storage with Amazon S3 and enhanced performance through CloudFront. Then, the cloud storage service Amazon S3 will host the website's static files, ensuring high availability and scalability. Use Google Cloud Storage to store and manage the data.

Cloud Computing 40

Cloud Computing Cloud Project Google Cloud 40

Cloudera

JUNE 5, 2023

On May 3, 2023, Cloudera kicked off a contest called “Best in Flow” for NiFi developers to compete to build the best data pipelines. The contest challenged developers to build data pipelines that represent their business use cases using Cloudera DataFlow. On the verge of the release of NiFi 2.0, Congratulations Vince!

Google Cloud 77

Google Cloud Cloud Storage Data Lake Data Pipeline 77

Towards Data Science

JULY 21, 2023

Like bean dip and ogres , layers are the building blocks of the modern data stack. Its powerful selection of tooling components combine to create a single synchronized and extensible data platform with each layer serving a unique function of the data pipeline. Let’s dive into it. The content, not the bean dip.

Building 98

Building Business Intelligence BI Cloud Storage 98