Netflix Tech

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

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Process Data Pipeline Datasets Aggregated Data 84

phData: Data Engineering

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. With many data modeling methodologies and processes available, choosing the right approach can be daunting. Efficiency through being able to streamline data storage and retrieval processes.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

LinkedIn Engineering

JANUARY 19, 2024

This multi-entity handover process involves huge amounts of data updating and cloning. 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. A typical merger & acquisition scenario.

Recruitment 88

Recruitment Data Process Process Kafka 88

Engineering at Meta

MARCH 18, 2024

In this post, we present the design behind Logarithm, and show how it powers AI training debugging use cases. We designed the system to support service-level guarantees on log freshness, completeness, durability, query latency, and query result completeness. Each log line can have zero or more metadata key-value pairs attached to it.

Engineering 91

Engineering Metadata Architecture Designing 91

Grouparoo

FEBRUARY 9, 2022

ETL (Extract, Transform, Load) refers to the process of extracting data from source systems, transforming it into appropriate formats, and loading it into a target system. ETL testing is the process of verifying and validating the data being moved to ensure that it is extracted, transformed, and loaded correctly into the destination system.

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AltexSoft

NOVEMBER 23, 2021

The traditional way of data integration involves consolidating disparate data within a single repository — commonly a data warehouse — via the extract, transform, load (ETL) process. If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT.

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Process Data Lake Metadata Data Warehouse 69

Monte Carlo

MAY 14, 2024

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. For example, a single table named ‘Customers’ is actually an aggregation of metadata that manages and references several data files, ensuring that the table behaves as a cohesive unit.

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Data Lake Metadata Hadoop Data Governance 52

Precisely

MAY 13, 2024

With these factors and more in mind, starting the process can feel overwhelming – but not to worry. Whether you’re bringing a new system online or connecting an existing database with your analytics platform, the process should be simple and straightforward. How much time is required from me for this process? #3.

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Rockset

MARCH 18, 2024

Flink is one of the most popular stream processing technologies, ranked as a top five Apache project and backed by a diverse committer community including Alibaba and Apple. It powers steam processing at many companies including Uber, Netflix, and Linkedin. They also need to be designed for real-time updates.

Cloud 64

Cloud Building Metadata Kafka 64

Netflix Tech

NOVEMBER 14, 2023

In this three-part blog post series, we introduce you to Psyberg , our incremental data processing framework designed to tackle such challenges! We’ll discuss batch data processing, the limitations we faced, and how Psyberg emerged as a solution. Let’s dive in! What is late-arriving data? How does late-arriving data impact us?

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Data Engineering Data Engineer Engineering Metadata 105

Netflix Tech

MARCH 10, 2023

This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts. During this evolution, quite often we receive requests to update the existing assets metadata or add new metadata for the new features added.

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Management Kafka Metadata Media 94

The Modern Data Company

JANUARY 22, 2024

The central piece to this narrative is DataOS®, Modern’s state-of-the-art data operating system designed to unify data across silos on a scalable level. Data products contain data and its metadata, code, and infrastructure so that each product is self-contained and usable independently.

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Data Integration Metadata Government Unstructured Data 98

AltexSoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics. Apache Spark components.

Big Data 52

Big Data Data Process Process Hadoop 52

Data Engineering Podcast

DECEMBER 18, 2022

Summary One of the reasons that data work is so challenging is because no single person or team owns the entire process. This introduces friction in the process of collecting, processing, and using data. Atlan is the metadata hub for your data ecosystem. Struggling with broken pipelines? Stale dashboards? Missing data?

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Metadata Business Intelligence Data Lake BI 130

Ascend.io

APRIL 22, 2024

A unified platform that combines a powerful metadata core, an extensible plugin architecture, DataAware automation, and multiple AI Assistants. Lack of unified metadata makes it difficult to optimize pipelines, avoid redundant processing, and quantify data engineering costs.

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Project Metadata Data Pipeline Data Engineering 52

RandomTrees

APRIL 18, 2024

In this article, we’ll explore the process of converting Informatica code to Talend code using the power of Python scripting. Understanding the Challenge Informatica PowerCenter has long been a favoured tool for Extract, Transform, Load (ETL) processes , offering a robust graphical interface for designing workflows and transformations.

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Coding Metadata Retail Python 52

Maxime Beauchemin

JANUARY 7, 2018

Batch data processing  — historically known as ETL —  is extremely challenging. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. The greater the claim made using analytics, the greater the scrutiny on the process should be.

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Data Engineering Data Engineer Data Process Process 183

Data Engineering Podcast

OCTOBER 16, 2022

Pathway is a streaming database engine that embeds artificial intelligence into the storage, with functionality designed to support the spatiotemporal data that is crucial for shipping and logistics. Atlan is the metadata hub for your data ecosystem. What are the types of data that Pathway is designed to work with?

Database 100

Database Metadata MongoDB MySQL 100

Data Engineering Podcast

JULY 17, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. How have the design and goals of the platform changed or evolved since you started working on it?

Data Management 130

Data Management Management Metadata MongoDB 130

Christophe Blefari

MARCH 1, 2023

You can also add metadata on models (in YAML). This is a methodology that has been designed more than 20 years ago that optimise the storage used. Also dbt only does a pass-through to your underlying data compute technology, there is not any kind of processing within dbt. You have to define sources in YAML files.

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Data Warehouse SQL Metadata Raw Data 278

LinkedIn Engineering

OCTOBER 5, 2023

The benefit of this component is that the consolidation of various objective functions into a single model enhances task transfer learning and expedites the individual task learning process. There’s no reason why the item embeddings will be aligned with this model, even if the embedding dimension is the same from one version to the next.

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IT Metadata Designing Algorithm 133

Data Engineering Podcast

SEPTEMBER 25, 2022

In this episode Tom Baeyens explains their reasons for creating a new syntax for expressing and validating checks for data assets and processes, as well as how to incorporate it into your own projects. Atlan is the metadata hub for your data ecosystem. And don’t forget to thank them for their continued support of this show!

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Building Metadata MongoDB MySQL 100

The Modern Data Company

JANUARY 22, 2024

The central piece to this narrative is DataOS®, Modern’s state-of-the-art data operating system designed to unify data across silos on a scalable level. Data products contain data and its metadata, code, and infrastructure so that each product is self-contained and usable independently.

Data Integration 87

Data Integration Metadata Government Unstructured Data 87

Snowflake

AUGUST 22, 2023

The data product lifecycle includes the following stages: Discovery Design Development Deployment Let’s take a look at what they entail and the roles that lead and support each of them: Discovery starts the process. A prioritization matrix can help formalize this process. Design adds shape to the concept.

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Metadata Data AWS Business Analyst 73

AltexSoft

MAY 27, 2021

The documents often come in semi-structured and unstructured data formats, which makes them difficult to process quickly and accurately. That’s when intelligent document processing or IDP enters the game. This post is a perfect place to get acquainted with intelligent document processing.

Technology 64

Technology Process Insurance Medical 64

Tweag

APRIL 19, 2023

Most training pipelines and systems are designed to handle fairly small, sub-megapixel images. To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. A single hospital makes many captures a day, producing terabytes of such data to store and process.

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Medical Process Cloud Bytes 60

Snowflake

APRIL 11, 2024

Processing over 4 billion queries running daily on our platform (as of January 31, 2024) [ 1 ] gives us unparalleled insights into the most complex data challenges. Seamless AI within Snowflake Copilot is a complete solution designed to seamlessly integrate with your existing Snowflake workflow.

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SQL AWS Data Analysis High Quality Data 137

Data Engineering Podcast

SEPTEMBER 11, 2022

Ananth Packildurai created Schemata as a way to make the creation of schema contracts a lightweight process, allowing the dependency chains to be constructed and evolved iteratively and integrating validation of changes into standard delivery systems. Atlan is the metadata hub for your data ecosystem.

Systems 100

Systems Metadata Building MongoDB 100

Precisely

MAY 23, 2024

It does very little good, however, to spot a fraudulent transaction after it’s already been processed. Redeployment of applications to the cloud, likewise, should be achievable without major disruption to your integration design. That’s where real-time integration makes a difference.

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Data Integration Metadata Telecommunication Data Governance 62

Data Engineering Podcast

JUNE 17, 2023

By incorporating column-level lineage in the data modeling process it encourages a more robust and well-informed design. In this episode Satish Jayanthi explores the benefits of incorporating column-aware tooling in the data modeling process. What are some of the ways that that information can be fed into other contexts?

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Data Lake Metadata Machine Learning Data Architecture 130

Data Engineering Weekly

DECEMBER 10, 2023

link] Evidently: ML system design - 300 case studies to learn from An amazing compilation of ML system design articles from various companies. link] Netflix: Diving Deeper into Psyberg: Stateless vs Stateful Data Processing Netflix wrote a deep-dive article about Psyberg’s incremental data processing pipeline framework.

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Data Engineering Data Engineer Engineering Metadata 76

Data Engineering Podcast

JUNE 26, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. How have the design and goals shifted or evolved since you started working on Unfolded?

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Datasets Unstructured Data Metadata MongoDB 130

AltexSoft

DECEMBER 23, 2022

But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the main reasons behind this is the need to timely process huge volumes of data in any format.

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Process Building Raw Data Data Lake 52

Data Engineering Podcast

FEBRUARY 26, 2023

Jean-Georges Perrin was tasked with designing a new data platform implementation at PayPal and wound up building a data mesh. In this episode he shares that journey and the combination of technical and organizational challenges that he encountered in the process.

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Building Metadata Machine Learning Data Integration 147

ThoughtSpot

MAY 31, 2023

Looking more broadly, we’ll also describe the security process we follow during any application iteration or enhancement, so you can see the great lengths we take to keep your data secure. The architecture is designed to be resilient against new-age attacks against LLMs like prompt injection and prompt leaks.

Data Security 86

Data Security Metadata Transportation Data Warehouse 86

Cloudera

JULY 13, 2023

Iceberg is an emerging open-table format designed for large analytic workloads. In this blog, we will discuss performance improvement that Cloudera has contributed to the Apache Iceberg project in regards to Iceberg metadata reads, and we’ll showcase the performance benefit using Apache Impala as the query engine.

Metadata 70

Metadata Java PostgreSQL Data Warehouse 70

Knowledge Hut

MAY 26, 2024

It was designed by Google to manage and schedule containers at scale. The system is designed to simplify life cycle management, allowing developers to focus on their application code rather than infrastructure maintenance. As a result, Kubernetes can greatly simplify the process of managing server deployments.

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Metadata Certification Accessible Accessibility 52