Knowledge Hut

JULY 3, 2023

Conventional batch processing techniques seem incomplete in fulfilling the demand of driving the commercial environment. This is where real-time data ingestion comes into the picture. Data is collected from various sources such as social media feeds, website interactions, log files and processing.

Data Ingestion 52

Data Ingestion Google Cloud Pipeline-centric Media 52

Data Engineering Weekly

MARCH 23, 2025

The author emphasizes the importance of mastering state management, understanding "local first" data processing (prioritizing single-node solutions before distributed systems), and leveraging an asset graph approach for data pipelines. and then to Nuage 3.0, The article highlights Nuage 3.0's

Data Engineering 63

Data Engineering Data Engineer Engineering Data 63

Data Engineering Weekly

APRIL 20, 2025

It makes me think, what could the impact of a similar system design be in a Lakehouse architecture? We all know that data freshness plays a critical role in the performance of Lakehouse. Apache Hudi, for example, introduces an indexing technique to Lakehouse.

Data Engineering 68

Data Engineering Data Engineer Engineering Kafka 68

Databand.ai

JULY 19, 2023

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Snowflake

OCTOBER 16, 2024

The company quickly realized maintaining 10 years’ worth of production data while enabling real-time data ingestion led to an unscalable situation that would have necessitated a data lake. Data scientists also benefited from a scalable environment to build machine learning models without fear of system crashes.

Digital Media 105

Digital Media Media Data Lake Data Warehouse 105

Databand.ai

AUGUST 30, 2023

DataOps Architecture: 5 Key Components and How to Get Started Ryan Yackel August 30, 2023 What Is DataOps Architecture? DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Data Engineering Weekly

MAY 16, 2023

In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive? I won’t bore you with the importance of data quality in the blog. But before doing that, let's revisit some of the basic theories of the data pipeline.

Engineering 111

Engineering Kafka Data Pipeline Data Warehouse 111

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. Benjamin Kennedy, Cloud Solutions Architect at Striim, emphasizes the outcome-driven nature of data pipelines.

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Data Pipeline Designing Data Lake Data Warehouse 98

phData: Data Engineering

FEBRUARY 21, 2025

On-prem data warehouses can provide lower latency solutions for critical applications that require high performance and low latency. Many companies may choose an on-prem data warehousing solution for quicker data processing to enable business decisions. Data integrations and pipelines can also impact latency.

Cloud 52

Cloud Data Warehouse Amazon Web Services Data Ingestion 52

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 Datasets Aggregated Data Data Pipeline 52

Data Engineering Podcast

JULY 23, 2023

Summary Real-time data processing has steadily been gaining adoption due to advances in the accessibility of the technologies involved. To bring streaming data in reach of application engineers Matteo Pelati helped to create Dozer. What are the architectural "-ilities" that you are trying to optimize for?

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Building Machine Learning SQL Python 130

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

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Data Pipeline Architecture Data Lake Data Warehouse 52

Edureka

AUGUST 19, 2024

The Azure Databricks architecture is designed to become an incredibly robust framework in data analytics on the Microsoft Azure platform. High-level Architecture Conclusion Frequently Asked Questions Azure Databricks simplifies the data engineering and data science workflows.

Architecture 40

Architecture Data Lake Machine Learning Data Ingestion 40

Confluent

FEBRUARY 6, 2019

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. So how can the Kafka ecosystem help here?

Machine Learning 107

Machine Learning Python Kafka Java 107

Cloudera

FEBRUARY 15, 2024

Seeing the future in a modern data architecture The key to successfully navigating these challenges lies in the adoption of a modern data architecture.

Insurance 88

Insurance Banking Data Architecture Data Ingestion 88

Cloudera

JUNE 7, 2021

It calls out that Cloudera DataFlow “ includes streaming flow and streaming data processing unified with Cloudera Data Platform ”. While we supported multiple streaming engines, we could see that Flink was gaining a lot of traction in the industry and in the community.

Kafka 103

Kafka Data Ingestion Cloud Architecture 103

Ascend.io

JULY 28, 2023

As companies become more data-driven, the scope and complexity of data pipelines inevitably expand. Without a well-planned architecture, these pipelines can quickly become unmanageable, often reaching a point where efficiency and transparency take a backseat, leading to operational chaos. What Is Data Pipeline Architecture?

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

Cloudera

SEPTEMBER 5, 2018

While the Internet of Things (IoT) represents a significant opportunity, IoT architectures are often rigid, complex to implement, costly, and create a multitude of challenges for organizations. An Open, Modular Architecture for IoT. Key components of the end-to-end architecture.

Architecture 47

Architecture Machine Learning Cloud Data Security 47

Cloudera

AUGUST 26, 2021

While we walk through the steps one by one from data ingestion to analysis, we will also demonstrate how Ozone can serve as an ‘S3’ compatible object store. Learn more about the impacts of global data sharing in this blog, The Ethics of Data Exchange. Data ingestion through ‘s3’. Ozone Namespace Overview.

Data Science 113

Data Science Cloud Hadoop Metadata 113

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. As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

AUGUST 29, 2023

Data lakes emerged as expansive reservoirs where raw data in its most natural state could commingle freely, offering unprecedented flexibility and scalability. This article explains what a data lake is, its architecture, and diverse use cases. Data warehouse vs. data lake in a nutshell.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Christophe Blefari

JANUARY 20, 2024

He wrote some years ago 3 articles defining data engineering field. Some concepts When doing data engineering you can touch a lot of different concepts. The main difference between both is the fact that your computation resides in your warehouse with SQL rather than outside with a programming language loading data in memory.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake.

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Data Lake Data Pipeline Unstructured Data Data 98

Snowflake

NOVEMBER 29, 2023

Figure 2: Questions answered by precision medicine Snowflake and FAIR in the world of precision medicine and biomedical research Cloud-based big data technologies are not new for large-scale data processing. A conceptual architecture illustrating this is shown in Figure 3.

Metadata 114

Metadata Healthcare Medical Data Storage 114

Snowflake

JULY 18, 2023

Data infrastructure that makes light work of complex tasks Built as a connected application from day one, the anecdotes Compliance OS uses the Snowflake Data Cloud for data ingestion and modeling, including a single cybersecurity data lake where all data can be analyzed within Snowflake.

Data Process 52

Data Process Process Data Lake Data Ingestion 52

Cloudera

MAY 30, 2024

Here’s what implementing an open data lakehouse with Cloudera delivers: Integration of Data Lake and Data Warehouse : An open data lakehouse brings together the best of both worlds by integrating the storage flexibility of a data lake with the query performance and structured querying capabilities of a data warehouse.

Data Lake 74

Data Lake Data Warehouse Programming Language Data Ingestion 74

Snowflake

JUNE 6, 2024

Schedule data ingestion, processing, model training and insight generation to enhance efficiency and consistency in your data processes. ” —Venky Yerneni, Manager, Solution Architecture, Weights & Biases Note: Snowflake Notebooks currently supports Python 3.9 with future updates coming soon.

SQL 115

SQL Python Machine Learning Data Workflow 115

ProjectPro

JANUARY 25, 2022

Features of PySpark The PySpark Architecture Popular PySpark Libraries PySpark Projects to Practice in 2022 Wrapping Up FAQs Is PySpark easy to learn? Here’s What You Need to Know About PySpark This blog will take you through the basics of PySpark, the PySpark architecture, and a few popular PySpark libraries , among other things.

Big Data 52

Big Data Data Process Process Kafka 52

ProjectPro

DECEMBER 28, 2021

Whether you're working with semi-structured, structured, streaming, or machine learning data, Apache Spark is a fast, easy-to-use framework that allows you to solve various complex data issues. Many traditional stream processing systems use a continuous operator model to process data.

Architecture 52

Architecture Kafka Java Scala 52

LinkedIn Engineering

OCTOBER 19, 2023

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

Process 119

Process Lambda Architecture Kafka Machine Learning 119

Confluent

OCTOBER 10, 2019

Most scenarios require a reliable, scalable, and secure end-to-end integration that enables bidirectional communication and data processing in real time. Let’s now take a look at the 10,000-foot view of a robust IoT integration architecture. End-to-end enterprise integration architecture. Inability to reprocess of events.

Kafka 20

Kafka Google Cloud Architecture Machine Learning 20

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. Apache Kafka is an event streaming platform that combines messages, storage, and data processing. Kafka Connect is a core component in event streaming architecture.

Kafka 21

Kafka SQL BI Hadoop 21

Databand.ai

JUNE 28, 2023

They’re betting their business on it and that the data pipelines that run it will continue to work. Context is crucial (and often lacking) A major cause of data quality issues and pipeline failures are transformations within those pipelines. Most data architecture today is opaque—you can’t tell what’s happening inside.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Cloudera

JUNE 16, 2022

Use cases like fraud detection, network threat analysis, manufacturing intelligence, commerce optimization, real-time offers, instantaneous loan approvals, and more are now possible by moving the data processing components up the stream to address these real-time needs. . Faster data ingestion: streaming ingestion pipelines.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

RandomTrees

MAY 6, 2025

Efficient data pipelines are necessary for AI systems to perform well since AI models need clean and organized as well as fresh datasets in order to learn and predict accurately. Au tomation in modern data engineering has a new dimension. It ensures a seamless flow of data within the pipelines with minimum human contact.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Cleanse 52

Snowflake

JUNE 5, 2024

Comparison of Snowflake Copilot and Cortex Analyst Cortex Search: Deliver efficient and accurate enterprise-grade document search and chatbots Cortex Search is a fully managed search solution that offers a rich set of capabilities to index and query unstructured data and documents. Our state-of-the-art hybrid search enables better results.

Coding 114

Coding Building Management Government 114

Maxime Beauchemin

JANUARY 20, 2017

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130