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

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

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

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?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

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

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.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

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 81

Digital Media Media Data Lake Data Warehouse 81

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

Rockset

MARCH 1, 2023

When you deconstruct the core database architecture, deep in the heart of it you will find a single component that is performing two distinct competing functions: real-time data ingestion and query serving. When data ingestion has a flash flood moment, your queries will slow down or time out making your application flaky.

Data Ingestion 144

Data Ingestion Database Architecture SQL 144

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

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

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 110

Engineering Kafka Data Pipeline Data Warehouse 110

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.

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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

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.

Data Lake 98

Data Lake Data Pipeline Unstructured Data Datasets 98

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

Striim

OCTOBER 11, 2024

These steps guarantee that data is accurate, reliable, and meaningful by the time it reaches its destination, making it possible for teams to generate insights and make data-driven decisions. This architecture can vary based on the needs of the organization and the type of data being processed.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Striim

JULY 31, 2024

Why Striim Stands Out As detailed in the GigaOm Radar Report, Striim’s unified data integration and streaming service platform excels due to its distributed, in-memory architecture that extensively utilizes SQL for essential operations such as transforming, filtering, enriching, and aggregating data.

Aggregated Data 52

Aggregated Data Data Ingestion Java Kafka 52

Cloudyard

JULY 22, 2024

Read Time: 2 Minute, 57 Second Previously, data engineers used Kinesis Firehose to transfer data into blob storage (S3) and then load it into Snowflake using either Snowpipe or batch processing. This introduced latency in the data pipeline for near real-time data processing.

AWS 52

AWS Data Ingestion Data Architecture 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

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Striim

JUNE 6, 2024

The volume of data itself can overwhelm processing capabilities, particularly in batch processing, which is slower than real-time processing. High latency, or delays in data processing and transmission, can result in major repercussions for businesses across various fronts.

Data Integration 52

Data Integration Data Ingestion Healthcare Data Pipeline 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 Engineer 130

Data Engineer Data Engineering Engineering Hadoop 130

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

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional who is in charge of designing, implementing, and maintaining data processing systems and solutions on the Microsoft Azure cloud platform. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

Data Engineer 52

Data Engineer Data Engineering Project Coding 52

Ascend.io

MAY 31, 2023

Table of Contents The Common Threads: Ingest, Transform, Share Before we explore the differences between the ETL process and a data pipeline , let’s acknowledge their shared DNA. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

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

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 95

SQL Python Machine Learning Data Workflow 95

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

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical.

Data Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 52

Striim

JUNE 18, 2024

Real-time data integration includes continuous and instantaneous processes for collecting, transforming, and distributing data across systems and applications. Data Transformation: This step involves filtering, aggregating, enriching, and other processes to prepare data for business use.

Data Integration 52

Data Integration Data Lake Retail Healthcare 52

Striim

JUNE 17, 2024

These roles will span various sectors, including data science, AI ethics, machine learning engineering, and AI-related research and development. Real-Time Data — The Missing Link What is Real-Time Data? ” Complexity and Cost Objection: Implementing real-time data systems is complex and costly.

Algorithm 52

Algorithm Retail Data Ingestion Healthcare 52

Knowledge Hut

NOVEMBER 2, 2023

They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems. Automating and optimizing software development lifecycle (SDLC) processes, CI/CD pipeline setup and management. Data modeling and analysis skills.

Data Engineer 52

Data Engineer Data Engineering Engineering Pipeline-centric 52

Ascend.io

JANUARY 2, 2024

The sources of data can be incredibly diverse, ranging from data warehouses, relational databases, and web analytics to CRM platforms, social media tools, and IoT device sensors. Regardless of the source, data ingestion, which usually occurs in batches or as streams, is the critical first step in any data pipeline.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

ProjectPro

FEBRUARY 6, 2023

Pub/Sub Use Cases Here are a few Google Cloud Pub/Sub use cases that will help you understand how to leverage this messaging service- Stream Processing Using Google Pub/Sub as input and output for stream processing systems such Spark, Flink, or Beam can be helpful for real-time data processing and analysis.

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 52

Knowledge Hut

MARCH 19, 2024

It allows businesses to construct event-driven architectures and microservices in which functions are invoked by events like file uploads, database changes, or HTTP requests. Lambda usage includes real-time data processing, communication with IoT devices, and execution of automated tasks.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

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 87

Metadata Healthcare Medical Data Storage 87