Monte Carlo

NOVEMBER 21, 2024

Whether it’s customer transactions, IoT sensor readings, or just an endless stream of social media hot takes, you need a reliable way to get that data from point A to point B while doing something clever with it along the way. That’s where data pipeline design patterns come in. Batch Processing Pattern 2.

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

ProjectPro

JUNE 6, 2025

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 40

Data Pipeline Architecture Kafka Data Lake 40

ProjectPro

JUNE 6, 2025

According to Bill Gates, “The ability to analyze data in real-time is a game-changer for any business.” ” Thus, don't miss out on the opportunity to revolutionize your business with real-time data processing using Azure Stream Analytics. It supports TLS 1.2 How Does Azure Stream Analytics Work?

Data Process 40

Data Process Process Data Ingestion BI 40

Data Engineering Podcast

JANUARY 7, 2024

Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up.

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Data Process Process Data Lake High Quality Data 147

Towards Data Science

FEBRUARY 12, 2024

In this first article, we’re exploring Apache Beam, from a simple pipeline to a more complicated one, using GCP Dataflow. Let’s learn what… Continue reading on Towards Data Science »

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Data Pipeline Data Process Process Data Science 98

Snowflake

JUNE 4, 2024

Snowflake’s new Python API (GA soon) simplifies data pipelines and is readily available through pip install snowflake. Finally, Tasks Backfill (PrPr) automates historical data processing within Task Graphs. Additionally, Dynamic Tables are a new table type that you can use at every stage of your processing pipeline.

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Data Pipeline Python SQL Government 122

ProjectPro

JUNE 6, 2025

PySpark Filter is used in conjunction with the Data Frame to filter data so that just the necessary data is used for processing, and the rest can be scarded. This allows for faster data processing since undesirable data is cleansed using the filter operation in a Data Frame.

Big Data 40

Big Data Data Process Process Kafka 40

Hevo

JUNE 6, 2025

Tired of wasting hours on repetitive data tasks? Scaling businesses experience complex data pipelines and large volumes of data. From data ingestion, transformation, and storage, ETL workflows can become extensive. Manual workflows don’t fit the bill and are prone to errors and inconsistencies.

Data Process 40

Data Process Process Data Ingestion Data Pipeline 40

Striim

SEPTEMBER 11, 2024

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. We’ll answer the question, “What are data pipelines?” Table of Contents What are Data Pipelines?

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Databand.ai

JUNE 28, 2023

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability.

Data Pipeline 98

Data Pipeline Data Engineer Data Engineering Engineering 98

Precisely

DECEMBER 28, 2023

Business success is based on how we use continuously changing data. That’s where streaming data pipelines come into play. This article explores what streaming data pipelines are, how they work, and how to build this data pipeline architecture. What is a streaming data pipeline?

Data Pipeline 91

Data Pipeline Building Kafka NoSQL 91

ProjectPro

JUNE 6, 2025

Looking for an efficient tool for streamlining and automating your data processing workflows? Let's consider an example of a data processing pipeline that involves ingesting data from various sources, cleaning it, and then performing analysis. Apache Airflow DAGs are your one-stop solution!

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Data Pipeline PostgreSQL Python Database 63

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). not sponsored.

Data Pipeline 98

Data Pipeline AWS Amazon Web Services Python 98

ProjectPro

JUNE 6, 2025

Efficient Scheduling and Runtime Increased Adaptability and Scope Faster Analysis and Real-Time Prediction Introduction to the Machine Learning Pipeline Architecture How to Build an End-to-End a Machine Learning Pipeline? Is python suitable for machine learning pipeline design patterns?

Machine Learning 58

Machine Learning Building Amazon Web Services AWS 58

Towards Data Science

MARCH 6, 2023

On-premise and cloud working together to deliver a data product Photo by Toro Tseleng on Unsplash Developing a data pipeline is somewhat similar to playing with lego, you mentalize what needs to be achieved (the data requirements), choose the pieces (software, tools, platforms), and fit them together. Google Cloud.

Google Cloud 98

Google Cloud Cloud Storage Data Pipeline Cloud 98

dbt Developer Hub

APRIL 30, 2025

Introduction Building scalable data pipelines in a fast-growing fintech can feel like fixing a bike while riding it. You must keep insights flowing even as data volumes explode. Traditional batch ETL (rebuilding entire tables each run) started to buckle; pipelines took hours, and costs ballooned.

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Data Pipeline Finance Banking Data 40

Netflix Tech

NOVEMBER 14, 2023

Understanding the nature of the late-arriving data and processing requirements will help decide which pattern is most appropriate for a use case. Stateful Data Processing : This pattern is useful when the output depends on a sequence of events across one or more input streams.

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Data Process Process Metadata Finance 96

ProjectPro

JUNE 6, 2025

Data engineering is the foundation for data science and analytics by integrating in-depth knowledge of data technology, reliable data governance and security, and a solid grasp of data processing. Data engineers need to meet various requirements to build data pipelines.

AWS 52

AWS Data Engineer Data Engineering Project 52

ProjectPro

JUNE 6, 2025

Project Idea : Use the StatsBomb Open Data to study player and team performances. Build a data pipeline to ingest player and match data, clean it for inconsistencies, and transform it for analysis. Load raw data into Google Cloud Storage, preprocess it using Mage VM, and store results in BigQuery.

Data Engineer 59

Data Engineer Data Engineering Project Engineering 59

Snowflake

NOVEMBER 11, 2024

Here’s how Snowflake Cortex AI and Snowflake ML are accelerating the delivery of trusted AI solutions for the most critical generative AI applications: Natural language processing (NLP) for data pipelines: Large language models (LLMs) have a transformative potential, but they often batch inference integration into pipelines, which can be cumbersome.

Unstructured Data 98

Unstructured Data SQL AWS Healthcare 98

Data Engineering Podcast

NOVEMBER 27, 2022

Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines.

Data Process 100

Data Process Process Metadata Data Lake 100

Ascend.io

DECEMBER 4, 2024

As we look towards 2025, it’s clear that data teams must evolve to meet the demands of evolving technology and opportunities. In this blog post, we’ll explore key strategies that data teams should adopt to prepare for the year ahead. How effective are your current data workflows?

Data Pipeline 52

Data Pipeline Metadata Data Workflow Data 52

ProjectPro

JUNE 6, 2025

Explore Data Engineer Projects to Learn the Plumbing of Data Science Role and Responsibilities of a Data Engineer Prepare, handle, and supervise efficient data pipeline architectures. Build and deploy ETL/ELT data pipelines that can begin with data ingestion and complete various data-related tasks.

Data Engineer 58

Data Engineer Data Engineering Engineering Amazon Web Services 58

Monte Carlo

OCTOBER 31, 2024

AI data engineers are data engineers that are responsible for developing and managing data pipelines that support AI and GenAI data products. Essential Skills for AI Data Engineers Expertise in Data Pipelines and ETL Processes A foundational skill for data engineers?

Data Engineer 52

Data Engineer Data Engineering Engineering Unstructured Data 52

ProjectPro

JUNE 6, 2025

Building data pipelines is a core skill for data engineers and data scientists as it helps them transform raw data into actionable insights. You’ll walk through each stage of the data processing workflow, similar to what’s used in production-grade systems.

Python 40

Python Building PostgreSQL Data Pipeline 40

ProjectPro

JUNE 6, 2025

Data Collection The first step is to collect real-time data (purchase_data) from various sources, such as sensors, IoT devices, and web applications, using data collectors or agents. These collectors send the data to a central location, typically a message broker like Kafka. Apache NiFi With over 4.1k

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Data Ingestion Kafka Google Cloud AWS 40

ProjectPro

JUNE 6, 2025

However, building and maintaining a scalable data science pipeline comes with challenges like data quality , integration complexity, scalability, and compliance with regulations like GDPR. Table of Contents What is a Data Science Pipeline? Why is a Data Science Pipeline Important?

Data Science 40

Data Science Building AWS Data Lake 40

Ascend.io

OCTOBER 28, 2024

Tools like Python’s requests library or ETL/ELT tools can facilitate data enrichment by automating the retrieval and merging of external data. Read More: Discover how to build a data pipeline in 6 steps Data Integration Data integration involves combining data from different sources into a single, unified view.

Raw Data 52

Raw Data Aggregated Data Data Pipeline Data Validation 52

Ascend.io

DECEMBER 4, 2024

As we look towards 2025, it’s clear that data teams must evolve to meet the demands of evolving technology and opportunities. In this blog post, we’ll explore key strategies that data teams should adopt to prepare for the year ahead. How effective are your current data workflows?

Data Pipeline 52

Data Pipeline Metadata Data Workflow Data 52

ProjectPro

JUNE 6, 2025

Table of Contents What is Data Ingestion in a Data Engineering Project? Why do you need a Data Ingestion Layer in a Data Engineering Project? Types of Data Ingestion How does Data Ingestion Work in the Data Pipeline? Data Ingestion vs. ETL - How are they different?

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Data Ingestion Data Engineer Data Engineering Project 45

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. Apache Hive 3 features in the latest HDP 3.0

Data Engineer 45

Data Engineer Data Engineering Engineering Kafka 45

ProjectPro

JUNE 6, 2025

According to Fortune Business Insights , the global big data and analytics market is expected to grow from $348.21 billion by 2032, highlighting the critical need for efficient data pipeline management. Apache Airflow is an open-source platform for programming and managing data pipelines. billion in 2024 to $924.39

Pipeline-centric 40

Pipeline-centric Database-centric Data Pipeline Data Workflow 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 Engineer 49

Data Engineer Data Engineering Engineering Hadoop 49

DataKitchen

NOVEMBER 5, 2024

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. By systematically moving data through these layers, the Medallion architecture enhances the data structure in a data lakehouse environment.

Architecture 79

Architecture Raw Data Pipeline-centric Data Ingestion 79

ProjectPro

JUNE 6, 2025

Data engineering has become crucial to any modern organization's technology stack. The need for fast and efficient data processing is high, as companies increasingly rely on data to make business decisions and improve product quality. But what books should you read if you want to learn more about data engineering?

Data Engineer 45

Data Engineer Data Engineering Engineering Lambda Architecture 45

ProjectPro

JUNE 6, 2025

You can load data into your target system after extracting it from one system and transforming it to meet its requirements using an ETL tool. This blog will explore the three best AWS ETL tools—AWS Kinesis, AWS Glue, and AWS Data Pipeline- and some of their significant features.

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ETL Tools AWS Data Engineer Data Engineering 40

ProjectPro

JUNE 6, 2025

Real-time Data Processing Application The project aims to handle high-volume data streams in real time while ensuring precise and reliable outcomes. Bustle is a real-world example that processes massive volumes of site metric data in real-time by leveraging AWS service.

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AWS Project Food Cloud Computing 60