Dataflow vs. other stream, batch processing engines

Editor’s note: This is the third blog in a three-part series examining the internal Google history that led to Dataflow, how Dataflow works as a Google Cloud service, and here, how it compares and contrasts with other products in the marketplace.

To place Google Cloud’s stream and batch processing tool Dataflow in the larger ecosystem, we’ll discuss how it compares to other data processing systems. Each system that we talk about has a unique set of strengths and applications that it has been optimized for. We’re biased, of course, but we think that we’ve balanced these needs particularly well in Dataflow.

#google cloud platform #data analytics #data analytic

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Batch Processing vs Stream Processing | System Design Primer | Tech Primers

This video about batch processing and stream processing systems covers the following topics

⏱ Chapter Timestamps

0:00 - Agenda
1:00 - What is Batch?
1:15 - What is Stream?
1:32 - What is Micro-Batching?
2:19 - When to use batch processing
3:47 - When to use stream processing
6:16 - Use-case: Analytics Application
10:52 - Case study: Netflix Kinesis Data Streams
11:43 - Case study: Nasdaq’s Architecture using Amazon EMR & Amazon S3
12:36 - Summary

📌 Links

🔗 Netflix Case study: https://aws.amazon.com/solutions/case-studies/netflix-kinesis-data-streams/
🔗 Nasdaq Case study: https://aws.amazon.com/blogs/big-data/nasdaqs-architecture-using-amazon-emr-and-amazon-s3-for-ad-hoc-access-to-a-massive-data-set/

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#BatchVsStream #Streams #TechPrimers

#batch processing #stream processing

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics

Spark Structured Streaming – Stateful Streaming

Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Internals of Structured Streaming“. And this blog pertains to Stateful Streaming in Spark Structured Streaming. So let’s get started.

Let’s start from the very basic understanding of what is Stateful Stream Processing. But to understand that, let’s first understand what Stateless Stream Processing is.

In my previous blogs of this series, I’ve discussed Stateless Stream Processing.

You can check them before moving ahead – Introduction to Structured Streaming and Internals of Structured Streaming

#analytics #apache spark #big data and fast data #ml #ai and data engineering #scala #spark #streaming #streaming solutions #tech blogs #stateful streaming #structured streaming

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

• Performance: It delivers optimal performance.
• Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
• UI design: React Native enables you to design simple and responsive UI for your mobile app.
• 3rd party plugins: This framework supports 3rd party plugins.
• Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

• Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
• Near-native user experience: React Native provides several native components, and these are platform agnostic.
• The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Let's get to know Data Streaming: A dev's point of view

Streaming of data has become the need of the hour. But do we really know how stream processing exactly works? What are its benefits? Where and how to stream data in your big data architecture correctly? How to process streamed data efficiently? What challenges do we face when we move from batch processing to stream processing? What is Stateful stream processing and what is stateless stream processing? Which one to opt and when? Let’s address all these queries!

Stream processing: What, Why, Benefits

By definition “A stream is an unbounded continuous flow of data”. That means when we keep capturing the data from some user’s activity (supervision) or tracking some person’s health (medical) continuously, then we get the latest data in (near) real-time. Now, if we process a record of data as soon as we capture it then the processing happens in a real-time streaming fashion unlike waiting for a few hours to collect the data and processing it in bulk. In essence that is the whole concept of streaming.

If someone asks, why should we do it if we have already a batch processing system already in place working superbly? The question is genuine as we already are processing reliably in batch mode without any issues. But in batch mode, we are waiting for several hours before the processing starts to actually collect the data. Hence the data which came right now will be processed later when it is time. Till then, the data is just sitting idle which is just not right. Stream processing utilizes the time here rather waiting.

Stream Processing processes the data as soon as the data is arrived (real-time streaming) or processes it within some really negligible time (Micro-batching). Hence the data collection in real-time will be utilized.

#lambda architecture #microbatching #stream processing challanges #streami processing #streaming architecture #streaming challanges