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

FEBRUARY 20, 2024

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Fivetran Image courtesy of Fivetran.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

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

Snowflake

APRIL 9, 2025

By enabling advanced analytics and centralized document management, Digityze AI helps pharmaceutical manufacturers eliminate data silos and accelerate data sharing. KAWA Analytics Digital transformation is an admirable goal, but legacy systems and inefficient processes hold back many companies efforts.

Pharmaceutical 97

Pharmaceutical Manufacturing Data Ingestion SQL 97

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

Ascend.io

OCTOBER 28, 2024

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis. This is crucial for maintaining data integrity and quality.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

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

Snowflake

JULY 18, 2023

For many businesses, gathering compliance data means manually collecting PDFs and screenshots. That’s a slow and laborious process, but anecdotes AI streamlines compliance and eliminates redundant work with its advanced compliance data infrastructure. The Data Cloud unlocks massive go-to-market opportunities.”

Data Process 52

Data Process Process Data Lake Data Ingestion 52

phData: Data Engineering

FEBRUARY 21, 2025

Prior to making a decision, an organization must consider the Total Cost of Ownership (TCO) for each potential data warehousing solution. On the other hand, cloud data warehouses can scale seamlessly. On-prem data warehouses can provide lower latency solutions for critical applications that require high performance and low latency.

Cloud 52

Cloud Data Warehouse Amazon Web Services Data Ingestion 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. link] Grab: Improving Hugo's stability and addressing oncall challenges through automation.

Data Engineering 62

Data Engineering Data Engineer Engineering Data 62

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 117

IT Data Ingestion Data AWS 117

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

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 102

Digital Media Media Data Lake Data Warehouse 102

phData: Data Engineering

NOVEMBER 8, 2024

Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms. These systems are built on open standards and offer immense analytical and transactional processing flexibility. Why should we use it?

Architecture 52

Architecture Systems Data Lake Google Cloud 52

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 110

Engineering Kafka Data Pipeline Data Warehouse 110

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

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 110

Manufacturing Data Warehouse Kafka Retail 110

Data Engineering Weekly

APRIL 20, 2025

Data engineering is one aspect where I see a few startups starting to disrupt. One of the core challenges of data engineering, as the author put it elegantly, The core difficulty lies in the fact that each step in the process requires specialized domain knowledge.

Data Engineering 67

Data Engineering Data Engineer Engineering Kafka 67

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 118

Data Pipeline Building Manufacturing Data Warehouse 118

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 121

Data Pipeline Python SQL Database 121

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

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 103

Kafka Data Ingestion Cloud Architecture 103

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 107

Metadata Healthcare Medical Data Storage 107

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 114

SQL Python Machine Learning Data Workflow 114

Cloudera

MAY 30, 2024

One of the primary benefits of deploying AI and analytics within an open data lakehouse is the ability to centralize data from disparate sources into a single, cohesive repository. It provides flexibility in storing both raw and processed data, allowing organizations to adapt to changing data requirements and analytical needs.

Data Lake 74

Data Lake Data Warehouse Programming Language Data Ingestion 74

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 96

Building Transportation Java Metadata 96

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

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

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 88

Insurance Banking Data Architecture Data Ingestion 88

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 Data 98

Towards Data Science

MAY 3, 2023

One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. VDK ingests data from the Data Source.

Data Lake 98

Data Lake SQL Data Data Warehouse 98