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 SQL 88

Cloudyard

FEBRUARY 19, 2024

Read Time: 1 Minute, 42 Second In this blog post, we’ll delve into a practical example that showcases the prowess of Snowpark by processing customer invoice data from a CSV file and handling credit card details from a JSON source. The journey begins with customer invoice data stored in a CSV file.

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Data Process Process Data Workflow Data 40

Ascend.io

DECEMBER 4, 2024

Examples include “reduce data processing time by 30%” or “minimize manual data entry errors by 50%.” Start Small and Scale: Instead of overhauling all processes at once, identify a small, manageable project to automate as a proof of concept. How effective are your current data workflows?

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Data Pipeline Metadata Data Workflow Data 52

Engineering at Meta

FEBRUARY 4, 2025

We created data logs as a solution to provide users who want more granular information with access to data stored in Hive. In this context, an individual data log entry is a formatted version of a single row of data from Hive that has been processed to make the underlying data transparent and easy to understand.

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Accessibility Accessible Raw Data Data Warehouse 92

Data Engineering Weekly

FEBRUARY 2, 2025

Notably, the process includes an RL step to create a specialized reasoning model (R1-Zero) capable of excelling in reasoning tasks without labeled SFT data, highlighting advancements in training methodologies for AI models. link] Get Your Guide: From Snowflake to Databricks: Our cost-effective journey to a unified data warehouse.

Data Engineering 57

Data Engineering Data Engineer Engineering Data Lake 57

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 115

SQL Python Machine Learning Data Workflow 115

Data Engineering Podcast

JANUARY 30, 2022

Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. What are some of the utility features that you have found most helpful for data processing? Pandas is a tool that spans data processing and data science.

Data Engineering 100

Data Engineering Data Engineer Engineering Python 100

Netflix Tech

NOVEMBER 14, 2023

In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing. Pipelines After Psyberg Let’s explore how different modes of Psyberg could help with a multistep data pipeline. In this case, the minimum hour to process the data is hour 2.

Metadata 102

Metadata Data Pipeline Scala Data Process 102

Edureka

APRIL 22, 2025

Since all of Fabric’s tools run natively on OneLake, real-time performance without data duplication is possible in Direct Lake mode. Because of the architecture’s ability to abstract infrastructure complexity, users can focus solely on data workflows.

BI 52

BI Pipeline-centric Data Lake Google Cloud 52

Data Engineering Podcast

AUGUST 28, 2022

What are the different concerns that need to be included in a stack that supports fully automated data workflows? There was recently an interesting article suggesting that the "left-to-right" approach to data workflows is backwards.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

DataKitchen

JANUARY 25, 2022

The dynamic nature of the consulting team meant that architectural decisions made at the data engineering level were often short-sighted and incoherent. The company incurred technical debt as consultants grafted one manually-driven exception process on top of another to adapt to evolving business requirements.

Consulting 97

Consulting Recruitment Data Lake Data Engineer 97

DataKitchen

APRIL 13, 2021

Testing and Data Observability. Process Analytics. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Reflow — A system for incremental data processing in the cloud.

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Consulting Machine Learning Data Science Data Pipeline 121

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

JULY 22, 2024

The benefits of migrating to Snowflake start with its multi-cluster shared data architecture, which enables scalability and high performance. Additional processing capability with SQL, as well as Snowflake capabilities like Stored Procedures, Snowpark , and Streams and Tasks, help streamline operations.

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Government Data Cloud Hadoop 118

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Ascend.io

JULY 3, 2024

This methodology emphasizes automation, collaboration, and continuous improvement, ensuring faster, more reliable data workflows. With data workflows growing in scale and complexity, data teams often struggle to keep up with the increasing volume, variety, and velocity of data. Let’s dive in!

Data Workflow 52

Data Workflow Data Pipeline Data Data Engineer 52

Ascend.io

JULY 3, 2024

Start small, then scale With data workflows growing in scale and complexity, data teams often struggle to keep up with the increasing volume, variety, and velocity of data. This is where DataOps comes ina methodology designed to streamline and automate data workflows, ensuring faster and more reliable data delivery.

Data Workflow 52

Data Workflow Data Pipeline Data Data Engineer 52

Hevo

SEPTEMBER 1, 2024

Managing and orchestrating data workflows efficiently is crucial in today’s data-driven world. As the amount of data constantly increases with each passing day, so does the complexity of the pipelines handling such data processes.

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Data Workflow Data Pipeline Data Data Process 52

Hevo

AUGUST 16, 2024

This blog explores the world of open source data orchestration tools, highlighting their importance in managing and automating complex data workflows. From Apache Airflow to Google Cloud Composer, we’ll walk you through ten powerful tools to streamline your data processes, enhance efficiency, and scale your growing needs.

Google Cloud 52

Google Cloud Data Workflow Data Data Engineer 52

Ascend.io

SEPTEMBER 6, 2024

AI-driven data quality workflows deploy machine learning to automate data cleansing, detect anomalies, and validate data. Integrating AI into data workflows ensures reliable data and enables smarter business decisions. Data quality is the backbone of successful data engineering projects.

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Data Cleanse Data Workflow Data Pipeline Machine Learning 52

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. By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes.

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Data Cleanse Data Pipeline Data Ingestion Data Validation 52

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

Ascend.io

DECEMBER 11, 2023

What Is Data Orchestration? Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. Data pipeline orchestration is characterized by a detailed understanding of pipeline events and processes.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

Workfall

JULY 18, 2023

As your source data changes, you can update the materializations incrementally, rather than starting the entire transformation process from scratch. This saves time and resources by only updating the necessary portions of the transformed data. Moreover, DBT materializations can handle incremental updates.

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Datasets Entertainment Data Data Workflow 77

Data Engineering Weekly

DECEMBER 16, 2024

Evolution of Data Lake Technologies The data lake ecosystem has matured significantly in 2024, particularly in table formats and storage technologies. These systems address the increasing complexity of search queries, blending semantic understanding with precise ranking processes to deliver highly relevant results.

Data Engineering 62

Data Engineering Data Engineer Engineering Unstructured Data 62

Cloudyard

JUNE 11, 2024

Read Time: 1 Minute, 48 Second RETRY LAST: In modern data workflows, tasks are often interdependent, forming complex task chains. Ensuring the reliability and resilience of these workflows is critical, especially when dealing with production data pipelines.

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Raw Data Aggregated Data Data Pipeline Data Workflow 52

Data Engineering Weekly

SEPTEMBER 29, 2024

[link] Google: SQL Has Problems - We Can Fix Them - Pipe Syntax In SQL It was a good weekend read about the proposed pipe syntax in SQL, which is more similar to Unix pipes in terms of its core concept—sequential data flow and transformation. Unix pipes typically represent a physical flow of data between processes.

Data Engineering 62

Data Engineering Data Engineer Engineering SQL 62

Monte Carlo

JULY 9, 2024

Time-to-Insight: The total time elapsed from data generation to the point where it provides actionable insights. This metric encompasses data latency plus the time required for processing and analysis. However, to truly optimize data quality timeliness, continuous monitoring and reporting are essential.

Food 52

Food Data Pipeline Data Data Workflow 52

Meltano

OCTOBER 5, 2022

Data is becoming the world’s most valuable resource, according to an article in The Economist dating back to 2017. Since then, the way we compile, process, and store data has evolved significantly, and it continues to do so at incredible speed. The goal of DataOps is to speed up the process of deriving value from data.

Engineering 52

Engineering Raw Data Data Pipeline Data Warehouse 52

Cloudera

AUGUST 21, 2020

Businesses need to be able to ingest huge volumes of data from these data points as well as handle, process, and store this vast amount of data. Then they need to move to data separation so that they not only ingest the data but prepare the data so that it becomes processable.

Banking 62

Banking Kafka Cloud Storage Government 62

Data Engineering Weekly

DECEMBER 25, 2023

The Rising Impact of AI and Large Language Models 2023 witnessed a substantial impact of AI and large language models in data engineering. These technologies are increasingly automating processes like ETL, improving data quality management, and evolving the landscape of data tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 52

Ascend.io

DECEMBER 19, 2023

We’ll talk about when and why ETL becomes essential in your Snowflake journey and walk you through the process of choosing the right ETL tool. Our focus is to make your decision-making process smoother, helping you understand how to best integrate ETL into your data strategy. But first, a disclaimer.

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ETL Tools IT Data Pipeline Data Warehouse 52

Monte Carlo

APRIL 28, 2023

DuckDB’s parallel execution capabilities can help DBAs improve the performance of data processing tasks. Researchers : Academics and researchers working with large volumes of data use DuckDB to process and analyze their data more efficiently. What makes DuckDB different?

Data Warehouse 52

Data Warehouse Programming Language Database Datasets 52

Workfall

JUNE 12, 2023

It finds applications in database replication, where it captures changes from the source database and updates target databases, ensuring data consistency without manual updates. CDC also plays a crucial role in data integration and ETL processes. This keeps analytical systems up to date for accurate reporting and analysis.

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Telecommunication Metadata Healthcare Finance 80

Ascend.io

JULY 11, 2024

It enhances data quality, governance, and optimization, making data retrieval more efficient and enabling powerful automation in data engineering processes. As practitioners using metadata to fuel data teams, we at Ascend understand the critical role it plays in organizing, managing, and optimizing data workflows.

Metadata 52

Metadata IT Government High Quality Data 52

Monte Carlo

FEBRUARY 25, 2025

Data engineering design patterns are repeatable solutions that help you structure, optimize, and scale data processing, storage, and movement. They make data workflows more resilient and easier to manage when things inevitably go sideways. Batch or stream processing? Data lake or warehouse?

Designing 52

Designing Data Engineering Data Engineer Engineering 52

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52