Netflix Tech

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty This blog post will cover how Psyberg helps automate the end-to-end catchup of different pipelines, including dimension tables. In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing.

Metadata 97

Metadata Data Pipeline Scala Data Workflow 97

Databand.ai

JUNE 28, 2023

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability. To measure, but not track.

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Data Pipeline Data Engineering Data Engineer Engineering 98

Knowledge Hut

SEPTEMBER 29, 2023

This growth is creating a strong demand for data experts, especially Azure data engineers. But who are Azure data engineers, and what do they do? Moreover, what benefits can you expect from a career in Azure Data Engineering? Why Should You Get an Azure Data Engineer Certification?

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Certification Data Engineering Data Engineer Engineering 52

Data Engineering Weekly

NOVEMBER 6, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. I plan to write a series of blogs on Schemata and Data Contract in the coming weeks.

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Data Engineering Data Engineer Engineering Machine Learning 40

Workfall

JUNE 12, 2023

In this dynamic realm of data engineering, a monumental challenge takes centre stage: efficiently managing the ever-changing tides of real-time data. Data, the lifeblood of organisations, holds the key to unlocking untapped potential and propelling businesses forward. Where Is CDC Used and Who Uses It?

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Cloudyard

APRIL 4, 2024

Read Time: 2 Minute, 13 Second Consider a scenario, you are part of data engineering team at a retail company. However, the volume of daily transaction data poses challenges in effectively segmenting customers and optimizing engagement. Traditional segmentation methods fall short in scalability and real-time insights.

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Retail Data Ingestion Metadata Datasets 40

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. We will explore how to create catalogs and tables and show examples of how to write and read data from these Iceberg tables.

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Process SQL Kafka Database 112

Databand.ai

JULY 19, 2023

This capability is particularly useful in complex data landscapes, where data may pass through multiple systems and transformations before reaching its final destination Impact analysis: When changes are made to data sources or data processing systems, it’s critical to understand the potential impact on downstream processes and reports.

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Pipeline-centric Data Governance Metadata Government 52

Databand.ai

AUGUST 30, 2023

Slow data processing: Due to the manual nature of many data workflows in legacy architectures, data processing can be time-consuming and resource-intensive. In a DataOps architecture, it’s crucial to have an efficient and scalable data ingestion process that can handle data from diverse sources and formats.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Cloudera

JUNE 18, 2022

The first generation of the Hive Metastore attempted to address the performance considerations to run SQL efficiently on a data lake. It provided the concept of a database, schemas, and tables for describing the structure of a data lake in a way that let BI tools traverse the data efficiently.

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Data Lake Data Warehouse BI SQL 90

AltexSoft

FEBRUARY 11, 2023

If you are not familiar with the above-mentioned concepts, we suggest you to follow the links above to learn more about each of them in our blog posts. Bad data management be like, Source: Makeameme Data architects are sometimes confused with other roles inside the data science team. Feel free to enjoy it.

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Data Architect Certification Generalist Big Data 87

Data Engineering Podcast

JUNE 29, 2020

This was a great conversation about the complexities of working in a niche domain of data analysis and how to build a pipeline of high quality data from collection to analysis. If you hand a book to a new data engineer, what wisdom would you add to it?

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Data Collection Management High Quality Data Metadata 100

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.

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Insurance Pharmaceutical Data Data Ingestion 67

Netflix Tech

OCTOBER 19, 2020

a Netflix member via Twitter This is an example of a question our on-call engineers need to answer to help resolve a member issue?—?which In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. The process started with manual pull of member account information that was part of the session.

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Building Transportation Metadata Java 93

DataKitchen

AUGUST 9, 2021

The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. The domain requires a team that creates/updates/runs the domain, and we can’t forget metadata: catalogs, lineage, test results, processing history, etc., …. DataOps Composability.

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Pharmaceutical Raw Data Data Lake Data 98

LinkedIn Engineering

DECEMBER 12, 2023

PEDAL also consists of a metadata store that holds various algorithmic parameters, including the scale of noise that we introduce and whether we should use one-shot or continual observation algorithms. The modified query or queries are then sent directly to Pinot to fetch the true results.

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Algorithm Metadata SQL Datasets 77

Cloudera

AUGUST 31, 2021

It outperforms other data warehouses on all sizes and types of data, including structured and unstructured, while scaling cost-effectively past petabytes. Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. Consideration of both data & metadata in the migration.

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Data Warehouse Database-centric Metadata Cloud 80

Cloudera

SEPTEMBER 17, 2020

CDW now includes a tech preview of its out-of-the-box insight and visualization tool, Cloudera Data Visualization. Cloudera Data Visualization is in tech preview for CDW, with more backend engine integrations planned for the near future. Text data served up via Solr’s powerful analytics engine and APIs.

Unstructured Data 80

Unstructured Data Data Warehouse Pharmaceutical MySQL 80

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%

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Recruitment Data Process Process Kafka 88

Cloudera

SEPTEMBER 1, 2020

It is hard to believe if you have had previous experience with setting up, sizing, and deploying a distributed search engine service that this is possible. Solr is a standard and open source, commonly adopted text search engine with rich query APIs for performing analytics over text and other unstructured data.

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Cloud Storage Unstructured Data AWS Analytics Application 67

Knowledge Hut

OCTOBER 26, 2023

In this blog, we will show some interesting AWS project ideas for all professionals, including beginners, intermediate, and advanced. blog) easily using any of your preferred CMS. Creating Real-time Data Processing Application You can build a real-time data processing application using Amazon Kinesis along with Amazon Lambda.

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AWS Coding Project Cloud Computing 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. Late arriving facts Late arriving facts can be problematic with a strict immutable data policy.

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Data Engineering Data Engineer Data Process Process 183

Cloudera

AUGUST 30, 2018

For more on the data-driven journey, I would encourage you to view our Cloudera Now presentation. The rest of this blog is focused on how Cloudera Enterprise 6.0 You could think of it as an insight factory, your innovation environment, and the engine that powers your digital transformation. appeared first on Cloudera Blog.

Unstructured Data 40

Unstructured Data Machine Learning Data Warehouse BI 40

Knowledge Hut

JULY 26, 2023

They enable organizations to use data as an asset, resulting in greater operational efficiency, improved decision-making, and an edge over competitors in today's data-driven corporate world. Database applications also help in data-driven decision-making by providing data analysis and reporting tools.

Database 52

Database NoSQL Telecommunication MongoDB 52

Rockset

MARCH 18, 2024

That’s why we’re thrilled that Confluent Cloud is making it easier to use Flink, providing efficient and performant stream processing while saving engineers from complex infrastructure management. These pipelines take contextual data and feed it into an LLM to improve the relevancy of a response. What is RAG?

Cloud 64

Cloud Building Metadata Kafka 64

Rockset

JUNE 7, 2021

This often leads to data being pulled in batches anywhere from large batches pulled once a day to lots of small batches pulled frequently. The rule of thumb is that if you are looking to build a real-time data processing system then the push approach should be used. Any new files are then captured and their metadata stored too.

IT 40

IT Kafka Database MongoDB 40

Netflix Tech

DECEMBER 3, 2022

Its main purpose is to enable easy unit testing of your data pipelines, but it can technically be used in any other situations as a readable data format for small data sets. All the above commands are very likely to be described in separate future blog posts, but right now let’s focus on the dataflow sample command.

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Data Pipeline Scala Metadata Food 143

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.

Big Data 78

Big Data Data Management Management Metadata 78

Monte Carlo

NOVEMBER 21, 2022

What’s next for the future of data engineering? Each year, we chat with one of our industry’s pioneering leaders about their predictions for the modern data stack – and share a few of our own. Pro-tip: be sure to check out his talk from IMPACT: The Data Observability Summit.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Cloudera

DECEMBER 15, 2022

Compute engines in these CDP data services can access and process data sets in the Iceberg tables concurrently, with shared security and governance provided by our unique Cloudera Shared Data Experience ( SDX ). Only metadata will be regenerated. Data quality using table rollback.

Cloud 76

Cloud Metadata Google Cloud Data Warehouse 76

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. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

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Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Zalando Engineering

NOVEMBER 29, 2017

In this blog post, we share our experiences and lessons learned to run our real-time applications built with Kafka’s Streams API in production on Amazon Web Services (AWS). Therefore for any of the affected applications, we introduced a processing timeout, ensuring there was an upper bound on the time taken by any of our edge cases.

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Kafka AWS Amazon Web Services Utilities 40

Cloudera

MARCH 23, 2022

Apache Iceberg is a high-performance, open table format, born-in-the cloud that scales to petabytes independent of the underlying storage layer and the access engine layer. By being a truly open table format, Apache Iceberg fits well within the vision of the Cloudera Data Platform (CDP). What is Apache Iceberg? 2: Open formats.

Metadata 88

Metadata Data Architecture BI Machine Learning 88

ProjectPro

JULY 23, 2021

AWS (Amazon Web Services) is the world’s leading and widely used cloud platform, with over 200 fully featured services available from data centers worldwide. This blog presents some of the most unique and innovative AWS projects from beginner to advanced levels. Real-time Data Processing Application 7.

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AWS Project Amazon Web Services Cloud Computing 52

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

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AWS Scala Metadata Data Lake 98

Netflix Tech

DECEMBER 21, 2020

On the other hand, these optimizations themselves need to be sufficiently inexpensive to justify their own processing cost over the gains they bring. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

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Data Warehouse Metadata Algorithm Data 81

ProjectPro

AUGUST 24, 2021

Nevertheless, that is not the only job in the data world. Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project.

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Data Engineering Data Engineer Coding Project 52