DoorDash Engineering

NOVEMBER 21, 2022

Subscribe for weekly updates The solution to real-time processing of inventory changes The simplest approach to propagating inventory level changes in the database to the rest of the system may have been to invoke the service code to take actions every time something that affects the inventory table is called.

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Towards Data Science

APRIL 30, 2024

Balancing correctness, latency, and cost in unbounded data processing Image created by the author. Intro Google Dataflow is a fully managed data processing service that provides serverless unified stream and batch data processing. Apache Beam lets users define processing logic based on the Dataflow model.

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Towards Data Science

FEBRUARY 18, 2024

How to Stream and Apply Real-Time Prediction Models on High-Throughput Time-Series Data Photo by JJ Ying on Unsplash Most of the stream processing libraries are not python friendly while the majority of machine learning and data mining libraries are python based. An event is generated by a producer (e.g.

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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.

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Data Science Blog: Data Engineering

NOVEMBER 15, 2023

In addition to Business Intelligence (BI), Process Mining is no longer a new phenomenon, but almost all larger companies are conducting this data-driven process analysis in their organization. This aspect can be applied well to Process Mining, hand in hand with BI and AI.

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Netflix Tech

AUGUST 1, 2022

Data Mesh?—?A A Data Movement and Processing Platform @ Netflix By Bo Lei , Guilherme Pires , James Shao , Kasturi Chatterjee , Sujay Jain , Vlad Sydorenko Background Realtime processing technologies (A.K.A Once deployed, the pipeline performs the actual heavy lifting data processing work.

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Workfall

JUNE 27, 2023

It means that there is a high risk of data loss but Apache Kafka solves this because it is distributed and can easily scale horizontally and other servers can take over the workload seamlessly. We want to always have unified data which is broadcasted to all the client applications simultaneously without any latency.

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Confluent

MAY 9, 2019

Event-first thinking enables us to build a new atomic unit: the event. Four pillars of event streaming. Pillar 1 – Business function: Payment processing pipeline. Pillar 4 – Operational plane: Event logging, DLQs and automation. Journey to Event Driven – Part 2: Programming Models for the Event-Driven Architecture.

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Data Engineering Podcast

DECEMBER 31, 2018

Summary As more companies and organizations are working to gain a real-time view of their business, they are increasingly turning to stream processing technologies to fullfill that need. However, the storage requirements for continuous, unbounded streams of data are markedly different than that of batch oriented workloads.

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Striim

NOVEMBER 8, 2023

Data Mesh is revolutionizing event streaming architecture by enabling organizations to quickly and easily integrate real-time data, streaming analytics, and more. In this article, we will explore the advantages and limitations of data mesh, while also providing best practices for building and optimizing a data mesh with Striim.

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Confluent

FEBRUARY 13, 2019

Part 1 of this series discussed why you need to embrace event-first thinking, while this article builds a rationale for different styles of event-driven architectures and compares and contrasts scaling, persistence and runtime models. Event-driven architecture. Event-driven, reactive architecture. Being event first.

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Knowledge Hut

JANUARY 30, 2024

ITIL Processes ITIL comprises several processes that make it extremely adaptable, scalable, and diverse. These processes consist of activities with specified inputs, causes, and outputs. Let's look at some of the ITIL Processes and ideas that underpin them. This process is completed through five successive activities.

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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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ProjectPro

FEBRUARY 16, 2023

At the core of such applications lies the science of machine learning, image processing, computer vision, and deep learning. As an example, consider the Facial Image Recognition System, it leverages the OpenCV Python library for implementing image processing techniques. What is OpenCV Python?

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Data Engineering Podcast

NOVEMBER 20, 2021

With the improvements in streaming engines it is now possible to perform all of your data integration in near real time, but it can be challenging to understand the proper processing patterns to make that performant. Can you start by giving an overview of the state of the market for data lakes today?

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Knowledge Hut

JULY 24, 2023

The future of SQL (Structured Query Language) is a scalding subject among professionals in the data-driven world. As data generation continues to skyrocket, the demand for real-time decision-making, data processing, and analysis increases. It is also integrable with other programming languages like Python and R.

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Precisely

AUGUST 10, 2023

Data integrity : Leaders also recognize the need to improve the quality and integrity of their most important data. For many organizations, that means paying considerable attention to SAP master data and the processes for creating and managing that information.

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Process Data Integration Manufacturing Portfolio 52

Knowledge Hut

JANUARY 23, 2024

To stay on top of processes and synchronize all the elements involved, I turn to one of the key best practices in the rule book: Project Integration Management. Here’s what can be achieved through integration management: Processes and tasks can be organized and listed out. Get to know more about project description.

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Knowledge Hut

MAY 3, 2024

A new breed of ‘Fast Data’ architectures has evolved to be stream-oriented, where data is processed as it arrives, providing businesses with a competitive advantage. Dean Wampler (Renowned author of many big data technology-related books) Dean Wampler makes an important point in one of his webinars.

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Cloudera

JULY 18, 2022

In this blog we will conclude the implementation of our fraud detection use case and understand how Cloudera Stream Processing makes it simple to create real-time stream processing pipelines that can achieve neck-breaking performance at scale. Data decays! It has a shelf life and as time passes its value decreases. Apache Flink.

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Data Engineering Weekly

MARCH 8, 2023

Pathway is a Python framework for realtime data stream processing that handles updates for you. You can set up your processing pipeline, and Pathway will ingest the new streaming data points for you, sending you alerts in realtime. This portion of the data is called a window.

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Netflix Tech

NOVEMBER 14, 2023

In this context, managing the data, especially when it arrives late, can present a substantial challenge! In this three-part blog post series, we introduce you to Psyberg , our incremental data processing framework designed to tackle such challenges! What is late-arriving data? How does late-arriving data impact us?

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Netflix Tech

MARCH 10, 2023

Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case. After reading the asset ids using one of the ways, an event is created per asset id to be processed synchronously or asynchronously based on the use case.

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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.

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Data Engineering Podcast

APRIL 9, 2023

Summary The customer data platform is a category of services that was developed early in the evolution of the current era of cloud services for data processing. Beyond the core technology of the data warehouse, what are the other evolutions/innovations that allow for a CDP experience to be built on top of the core data stack?

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Data Engineering Weekly

JULY 14, 2024

Canva: How Canva collects 25 billion events per day Canva writes about its event collection infrastructure capabilities, handling 25 billion events per day (800 billion events per month) with 99.999% uptime. Streaming execution to process a small chunk of data at a time.

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Data Engineering Data Engineer Engineering Unstructured Data 98

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. Order of process phases. What is ELT?

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Data Engineering Weekly

NOVEMBER 11, 2024

Slack provides an excellent overview of this process. link] Hamel: Creating a LLM-as-a-Judge That Drives Business Results The author writes a comprehensive guide on creating an effective large language model (LLM) as a judge for evaluating AI products, focusing on the process of "Critique Shadowing."

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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. The blog is an excellent summary of the existing unstructured data landscape.

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Striim

OCTOBER 18, 2024

Multi-node deployments allow Striim to operate across several interconnected servers or nodes, each handling data processing, streaming, and analytics in tandem. Striim’s multi-node deployment increases redundancy by replicating workloads and data across several nodes.

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Data Engineering Weekly

OCTOBER 20, 2024

[link] Zach Wilson: How GenAI will impact data engineering An excellent overview of the potential impact of GenAI in data engineering workflow that can change the pipeline authoring, shipping pipeline, and maintaining the pipeline. There are tons of optimizations we can do to improve the efficiency.

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Monte Carlo

NOVEMBER 21, 2024

That’s where data pipeline design patterns come in. They’re basically architectural blueprints for moving and processing your data. So, why does choosing the right data pipeline design matter? In this guide, we’ll explore the patterns that can help you design data pipelines that actually work.

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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. This is compatible with both Python and Scala Spark.

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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.

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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?

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Snowflake

JUNE 4, 2024

For faster workflows, choose event-driven Triggered Tasks (PuPr) with 10-second latency or Low Latency Tasks (PrPr) for reduced task-scheduling intervals, down to 15 seconds. Finally, Tasks Backfill (PrPr) automates historical data processing within Task Graphs. Follow this quickstart to test-drive Dynamic Tables yourself.

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Data Pipeline Python SQL Government 103

Striim

OCTOBER 11, 2024

Then, we’ll explore a data pipeline example and dive deeper into the key differences between a traditional data pipeline vs ETL. What is a Data Pipeline? A data pipeline refers to a series of processes that transport data from one or more sources to a destination, such as a data warehouse, database, or application.

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