LinkedIn Engineering

JUNE 15, 2023

However, we found that many of our workloads were bottlenecked by reading multiple terabytes of input data. To remove this bottleneck, we built AvroTensorDataset , a TensorFlow dataset for reading, parsing, and processing Avro data. If greater than one, records in files are processed in parallel.

Datasets 102

Datasets Bytes Process Data Ingestion 102

Monte Carlo

MAY 28, 2024

A data ingestion architecture is the technical blueprint that ensures that every pulse of your organization’s data ecosystem brings critical information to where it’s needed most. Ensuring all relevant data inputs are accounted for is crucial for a comprehensive ingestion process.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Striim

NOVEMBER 13, 2023

Introduction In the fast-evolving world of data integration, Striim’s collaboration with Snowflake stands as a beacon of innovation and efficiency. As low as 3 seconds P95 latency with 158 gb/hr of Oracle CDC ingest. This method is particularly adept at handling large data sets securely and efficiently.

Data Ingestion 59

Data Ingestion Utilities Data Integration Data 59

Monte Carlo

MARCH 14, 2023

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Table of Contents What is Data Ingestion?

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

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

Team Data Science

MAY 10, 2020

I can now begin drafting my data ingestion/ streaming pipeline without being overwhelmed. With careful consideration and learning about your market, the choices you need to make become narrower and more clear. I'll use Python and Spark because they are the top 2 requested skills in Toronto.

Data Pipeline 130

Data Pipeline Data Ingestion AWS Kafka 130

ProjectPro

JANUARY 25, 2022

PySpark is a handy tool for data scientists since it makes the process of converting prototype models into production-ready model workflows much more effortless. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency. RDD uses a key to partition data into smaller chunks.

Big Data 52

Big Data Data Process Process Kafka 52

Striim

SEPTEMBER 11, 2024

With data volumes and sources rapidly increasing, optimizing how you collect, transform, and extract data is more crucial to stay competitive. That’s where real-time data, and stream processing can help. 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

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.

Machine Learning 107

Machine Learning Python Kafka Java 107

Data Engineering Weekly

MAY 16, 2023

It involves thorough checks and balances, including data validation, error detection, and possibly manual review. The bias toward correctness will increase the processing time, which may not be feasible when speed is a priority. Let’s talk about the data processing types.

Engineering 112

Engineering Kafka Data Pipeline Data Warehouse 112

Cloudera

JANUARY 20, 2021

We have simplified this journey into five discrete steps with a common sixth step speaking to data security and governance. The six steps are: Data Collection – data ingestion and monitoring at the edge (whether the edge be industrial sensors or people in a brick and mortar retail store). Data Collection Challenge.

Manufacturing 115

Manufacturing Data Warehouse Kafka Retail 115

Snowflake

MARCH 14, 2024

It sits on the application layer within SAP, which makes almost any structured data accessible and available for change data capture (CDC). Snowpipe Streaming allows Glue to get the data across to Snowflake very quickly. It also gives organizations a view of the combined changed data, committed data and data within Snowflake.

IT 99

IT Data Ingestion Data AWS 99

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 was your decision process for building Dozer as open source?

Building 130

Building Machine Learning SQL Python 130

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 81

Architecture Raw Data Pipeline-centric Data Ingestion 81

Cloudera

FEBRUARY 8, 2021

The first blog introduced a mock connected vehicle manufacturing company, The Electric Car Company (ECC), to illustrate the manufacturing data path through the data lifecycle. Having completed the Data Collection step in the previous blog, ECC’s next step in the data lifecycle is Data Enrichment.

Data Pipeline 122

Data Pipeline Building Manufacturing Data Warehouse 122

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. batch — Batch processing is at the core of data engineering. One of the major task is to move data from a source storage to a destination storage.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Cloudera

FEBRUARY 15, 2024

Data integration and ingestion: With robust data integration capabilities, a modern data architecture makes real-time data ingestion from various sources—including structured, unstructured, and streaming data, as well as external data feeds—a reality.

Insurance 93

Insurance Banking Data Architecture Data Ingestion 93

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 84

Digital Media Media Data Lake Data Warehouse 84

Cloudera

JUNE 7, 2021

CDF streamlines the process of collecting, curating and analyzing real-time streaming data with its integrated set of components. It calls out that Cloudera DataFlow “ includes streaming flow and streaming data processing unified with Cloudera Data Platform ”.

Kafka 105

Kafka Data Ingestion Cloud Architecture 105

Cloudera

APRIL 15, 2019

While Cloudera Flow Management has been eagerly awaited by our Cloudera customers for use on their existing Cloudera platform clusters, Cloudera Edge Management has generated equal buzz across the industry for the possibilities that it brings to enterprises in their IoT initiatives around edge management and edge data collection.

Management 17

Management Data Ingestion Data Collection Government 17

Netflix Tech

OCTOBER 19, 2020

Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. The process started with manual pull of member account information that was part of the session.

Building 95

Building Transportation Java Metadata 95

Data Engineering Podcast

OCTOBER 12, 2020

Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms.

Business Intelligence 100

Business Intelligence BI Consulting Data Ingestion 100

Maxime Beauchemin

JANUARY 20, 2017

This discipline also integrates specialization around the operation of so called “big data” distributed systems, along with concepts around the extended Hadoop ecosystem, stream processing, and in computation at scale. 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

Snowflake

JUNE 4, 2024

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. Follow this quickstart to test-drive Dynamic Tables yourself. Snowflake integrates with GitHub, GitLab, Azure DevOps and Bitbucket.

Data Pipeline 105

Data Pipeline Python SQL Government 105

Snowflake

JUNE 6, 2024

You can now use Snowflake Notebooks to simplify the process of connecting to your data and to amplify your data engineering, analytics and machine learning workflows. Schedule data ingestion, processing, model training and insight generation to enhance efficiency and consistency in your data processes.

SQL 99

SQL Python Machine Learning Data Workflow 99

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. What is the Difference Between Data Testing and Data Observability?

Data Lake 98

Data Lake Data Pipeline Unstructured Data Datasets 98

Snowflake

NOVEMBER 29, 2023

For example, the data storage systems and processing pipelines that capture information from genomic sequencing instruments are very different from those that capture the clinical characteristics of a patient from a site. A conceptual architecture illustrating this is shown in Figure 3.

Metadata 89

Metadata Healthcare Medical Data Storage 89

Confluent

MAY 7, 2019

Data processing requirements. Let’s first examine what we would want from a typical system that processes a large amount of data for a critical cause. We will put ourselves in the shoes of a fictional, yet very popular stock trading platform that uses Kafka to process trade orders from financial brokers.

Kafka 111

Kafka Accessible Accessibility Metadata 111

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Netflix Tech

DECEMBER 21, 2020

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. The Ursula based pipeline also performed file merges on the ingested table partitions periodically.

Data Warehouse 85

Data Warehouse Metadata Algorithm Data 85

DataKitchen

OCTOBER 20, 2023

The Challenge: High Stakes in the Age of Personalized Data Observability The primary challenge stems from the requirement of Data Consumers for personalized monitoring and alerts based on their unique data processing needs. Data Observability platforms often need to deliver this level of customization.

Insurance 72

Insurance Pharmaceutical Data Data Ingestion 72

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. Batch processing and reports after minutes or even hours is not sufficient. Apache Kafka is an event streaming platform that combines messages, storage, and data processing.

Kafka 21

Kafka SQL BI Hadoop 21

Hevo

JULY 10, 2024

While you can use Snowpipe for straightforward and low-complexity data ingestion into Snowflake, Snowpipe alternatives, like Kafka, Spark, and COPY, provide enhanced capabilities for real-time data processing, scalability, flexibility in data handling, and broader ecosystem integration.

Kafka 52

Kafka Data Ingestion Data Data Process 52

Cloudera

DECEMBER 15, 2022

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet , have built open lakehouses to future-proof their data platforms for all their analytical workloads. Cloudera partners are also benefiting from Apache Iceberg in CDP. ORC open file format support.

Cloud 85

Cloud Metadata Google Cloud Data Warehouse 85

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Striim

JUNE 6, 2024

Delays may occur in the transformation stage if data cleaning, enrichment, and formatting processes are not optimized. Inefficient data transfer protocols or high data volumes can exacerbate latency during the loading phase. What are the Consequences of High-Latency? What are the Consequences of High-Latency?

Data Integration 52

Data Integration Data Ingestion Healthcare Data Pipeline 52

Cloudera

JULY 21, 2022

The table below summarizes Hive and Druid key features and strengths and suggests how combining the feature sets can provide the best of both worlds for data analytics. Cloudera Data Warehouse). Efficient batch data processing. Complex data transformations. Native streaming ingestion support from Kafka and Kinesis.

BI 92

BI Digital Media Data Warehouse Kafka 92