LinkedIn Engineering

JANUARY 19, 2024

Data consistency, feature reliability, processing scalability, and end-to-end observability are key drivers to ensuring business as usual (zero disruptions) and a cohesive customer experience. With our new data processing framework, we were able to observe a multitude of benefits, including 99.9%

Recruitment 88

Recruitment Data Process Process Kafka 88

Data Engineering Podcast

NOVEMBER 27, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. to add support? to add support?

Data Process 100

Data Process Process Metadata Business Intelligence 100

Engineering at Meta

JANUARY 22, 2025

In this blog, we will delve into an early stage in PAI implementation: data lineage. Data lineage refers to the process of tracing the journey of data as it moves through various systems, illustrating how data transitions from one data asset, such as a database table (the source asset), to another (the sink asset).

Data Warehouse 118

Data Warehouse SQL Programming Language Data 118

Monte Carlo

FEBRUARY 12, 2024

Today, this first-party data mostly lives in two types of data repositories. If it is structured data then it’s often stored in a table within a modern database, data warehouse or lakehouse. If it’s unstructured data, then it’s often stored as a vector in a namespace within a vector database.

Database 72

Database Unstructured Data Data Pipeline Metadata 72

Ascend.io

NOVEMBER 14, 2024

Why Future-Proofing Your Data Pipelines Matters Data has become the backbone of decision-making in businesses across the globe. The ability to harness and analyze data effectively can make or break a company’s competitive edge. Set Up Auto-Scaling: Configure auto-scaling for your data processing and storage resources.

Data Pipeline 52

Data Pipeline Amazon Web Services Data Integration Data 52

Analytics Vidhya

FEBRUARY 7, 2023

Introduction Data engineering is the field of study that deals with the design, construction, deployment, and maintenance of data processing systems. The goal of this domain is to collect, store, and process data efficiently and efficiently so that it can be used to support business decisions and power data-driven applications.

Data Engineering 248

Data Engineering Data Engineer Engineering Data 248

Snowflake

APRIL 9, 2025

KAWA combines analytics, automation and AI agents to help enterprises build data apps and AI workflows quickly and achieve their digital transformation goals. It connects structured and unstructured databases across sources and uses a no-code UI or Python for advanced and predictive analytics.

Pharmaceutical 98

Pharmaceutical Manufacturing Data Ingestion SQL 98

Knowledge Hut

MARCH 14, 2024

Do you want a database system that can scale quickly and manage heavy workloads? Should that be the case, Azure SQL Database might be your best bet. Microsoft SQL Server's functionalities are fully included in Azure SQL Database, a cloud-based database service that also offers greater flexibility and scalability.

Database 52

Database SQL Relational Database BI 52

Striim

MARCH 21, 2025

Streaming cloud integration moves data continuously in real time between heterogeneous databases, with in-flight data processing. Read on, or watch the 9-minute video: Lets focus on how to use streaming data integration in cloud initiatives, and the five common scenarios that we see.

Cloud 52

Cloud Database Architecture BI 52

Striim

MARCH 21, 2025

Streaming cloud integration moves data continuously in real time between heterogeneous databases, with in-flight data processing. Read on, or watch the 9-minute video: Lets focus on how to use streaming data integration in cloud initiatives, and the five common scenarios that we see.

Cloud 52

Cloud Database Architecture BI 52

Data Engineering Weekly

OCTOBER 27, 2024

Astasia Myers: The three components of the unstructured data stack LLMs and vector databases significantly improved the ability to process and understand unstructured data. I never thought of PDF as a self-contained document database, but that seems a reality that we can’t deny.

Data Engineering 57

Data Engineering Data Engineer Engineering Unstructured Data 57

Jesse Anderson

DECEMBER 25, 2024

The conversation also explores the future of data processing with DuckDB and MotherDuck, highlighting the potential of single-node databases and the shift towards smaller, more efficient data solutions. Lastly, she has shared her perspectives on leadership, mentorship, and creating a more inclusive tech industry.

Google Cloud 130

Google Cloud Cloud Database Data Solutions 130

Snowflake

FEBRUARY 13, 2025

Out-of-the-box business continuity/disaster recovery: Snowflake enables customers to easily safeguard mission-critical accounts and data sets to maintain uptime. It's easy to use, there's no maintenance, and database administration is drastically reduced. It gives us functionality we can't get anywhere else and it costs us less.

Management 85

Management Government Cloud Unstructured Data 85

phData: Data Engineering

NOVEMBER 8, 2024

With the collective power of the open-source community, Open Table Formats remain at the cutting edge of data architecture, evolving to support emerging trends and addressing the limitations of previous systems. Amazon S3, Azure Data Lake, or Google Cloud Storage). Amazon S3, Azure Data Lake, or Google Cloud Storage).

Architecture 52

Architecture Systems Data Lake Google Cloud 52

Data Engineering Podcast

JUNE 9, 2024

Summary Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. Support Data Engineering Podcast Summary Streaming data processing enables new categories of data products and analytics.

Process 147

Process Data Lake High Quality Data Machine Learning 147

Snowflake

JUNE 4, 2024

This suite of APIs supports Tasks/DAG, Snowpark Container Services, Tables, Warehouse, Schema and Databases. 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.

Data Pipeline 121

Data Pipeline Python SQL Database 121

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

Netflix Tech

DECEMBER 17, 2024

Metric definitions are often scattered across various databases, documentation sites, and code repositories, making it difficult for analysts and data scientists to find reliable information quickly. We work with different platform data providers to get inventory , ownership , and usage data for the respective platforms theyown.

Engineering 85

Engineering Entertainment Amazon Web Services Utilities 85

Ascend.io

OCTOBER 28, 2024

Filling in missing values could involve leveraging other company data sources or even third-party datasets. The cleaned data would then be stored in a centralized database, ready for further analysis. This ensures that the sales data is accurate, reliable, and ready for meaningful analysis.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Striim

MARCH 21, 2025

Business transactions captured in relational databases are critical to understanding the state of business operations. Since the value of data quickly drops over time, organizations need a way to analyze data as it is generated. Traditionally, businesses used batch-based approaches to move data once or several times a day.

IT 52

IT Data Lake Relational Database Data Warehouse 52

Striim

MARCH 21, 2025

A streaming ETL for Snowflake approach loads data to Snowflake from diverse sources such as transactional databases, security systems logs, and IoT sensors/devices in real time , while simultaneously meeting scalability, latency, security, and reliability requirements.

Data Warehouse 52

Data Warehouse MongoDB MySQL Hadoop 52

Monte Carlo

OCTOBER 31, 2024

The foundational skills are similar between traditional data engineers and AI data engineers are similar, with AI data engineers more heavily focused on machine learning data infrastructure, AI-specific tools, vector databases, and LLM pipelines. Let’s dive into the tools necessary to become an AI data engineer.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 52

Marc Lamberti

SEPTEMBER 22, 2023

The Meta database: Database compatible with SqlAlchemy. Only the Scheduler and the meta database components are required to run Airflow. It is also essential to understand what Airflow is not – it’s neither a streaming solution nor a data processing framework.

Data Pipeline 130

Data Pipeline Python Metadata Database 130

DataKitchen

MARCH 20, 2025

Understanding this framework offers valuable insights into team efficiency, operational excellence, and data quality. Process-centric data teams focus their energies predominantly on orchestrating and automating workflows. Data-centric data teams perceive complexity as directly proportional to the number of tables they manage.

Pipeline-centric 65

Pipeline-centric Database-centric Process Data 65

Team Data Science

DECEMBER 17, 2020

It’s not a must for data scientist to have skill in data engineering before they can analyze data processed by data engineer or before they can move uniformly with other group (involving data engineer) for the progress of the company. Data scientists should acquire some basic SQL functionality.

Data Engineering 130

Data Engineering Data Engineer Engineering Cloud Computing 130

Rockset

MARCH 1, 2023

Every database built for real-time analytics has a fundamental limitation. 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.

Data Ingestion 144

Data Ingestion Database Architecture SQL 144

Hevo

APRIL 26, 2024

Most organizations find it challenging to manage data from diverse sources efficiently. Amazon Web Services (AWS) enables you to address this challenge with Amazon RDS, a scalable relational database service for Microsoft SQL Server (MS SQL). However, simply storing the data isn’t enough.

AWS 52

AWS Amazon Web Services Data Process Process 52

Analytics Vidhya

FEBRUARY 19, 2023

Introduction Data pipelines play a critical role in the processing and management of data in modern organizations. A well-designed data pipeline can help organizations extract valuable insights from their data, automate tedious manual processes, and ensure the accuracy of data processing.

Data Pipeline 199

Data Pipeline Data Designing Data Process 199

Snowflake

NOVEMBER 11, 2024

These scalable models can handle millions of records, enabling you to efficiently build high-performing NLP data pipelines. However, scaling LLM data processing to millions of records can pose data transfer and orchestration challenges, easily addressed by the user-friendly SQL functions in Snowflake Cortex.

Unstructured Data 98

Unstructured Data SQL AWS Healthcare 98

Data Engineering Weekly

FEBRUARY 18, 2025

Fluss is a compelling new project in the realm of real-time data processing. In contrast, Fluss adopts a Lakehouse-native design with structured tables, explicit schemas, and support for all kinds of data types; it directly mirrors the Lakehouse paradigm. The second difference is the Storage Model.

Kafka 73

Kafka Lambda Architecture SQL Architecture 73

Data Engineering Weekly

JANUARY 15, 2025

The Critical Role of AI Data Engineers in a Data-Driven World How does a chatbot seamlessly interpret your questions? The answer lies in unstructured data processing—a field that powers modern artificial intelligence (AI) systems. Experience with vector databases (e.g.,

Data Engineering 62

Data Engineering Data Engineer Unstructured Data Engineering 62

Confluent

SEPTEMBER 25, 2019

And, while this is fairly simple to comprehend, it raises a big question: Are traditional database architectures a good fit for this emerging world? Databases, after all, have been the most successful infrastructure layer in application development. Apache Kafka ® and its uses.

Database-centric 56

Database-centric Kafka Pipeline-centric Retail 56

Snowflake

APRIL 7, 2025

The Snowflake Native App Framework enables us to develop and deploy data-intensive applications directly within the Snowflake ecosystem. This integration allows us to leverage Snowflake's robust data processing and storage features, enabling our AI-driven compliance and quality management tools to operate efficiently and at scale.

Food 80

Food Data Transparency Software Engineering Software Engineer 80

Cloudyard

JANUARY 21, 2025

Read Time: 6 Minute, 6 Second In modern data pipelines, handling data in various formats such as CSV, Parquet, and JSON is essential to ensure smooth data processing. However, one of the most common challenges faced by data engineers is the evolution of schemas as new data comes in.

Data Schemas 59

Data Schemas Data Pipeline Data Warehouse Data Storage 59

Snowflake

OCTOBER 16, 2024

Snowflake’s flexible architecture and cost-effective per-second pricing lowered the company’s total cost of ownership by eliminating the need for a separate data lake, enabling greater innovation and resilience. One of its core products uses a single-tenant architecture, which means each client has its own database.

Digital Media 104

Digital Media Media Data Lake Data Warehouse 104

Knowledge Hut

APRIL 25, 2024

Big data is a term that refers to the massive volume of data that organizations generate every day. In the past, this data was too large and complex for traditional data processing tools to handle. There are a variety of big data processing technologies available, including Apache Hadoop, Apache Spark, and MongoDB.

Big Data 98

Big Data Technology Hadoop NoSQL 98

Cloudera

MARCH 2, 2023

Recently, we announced enhanced multi-function analytics support in Cloudera Data Platform (CDP) with Apache Iceberg. Iceberg is a high-performance open table format for huge analytic data sets. The Default Database is an optional field so we can leave it empty for now. The Catalog Type should be set to Hive. ssb_default`.`iceberg_hive_example`

Process 114

Process SQL Kafka Database 114