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#big-data-analytics #watermarking #spark-streaming #data-science

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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

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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.

In general, stateful stream processing is an application ** design pattern** for processing an

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

#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

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#big-data-analytics #watermarking #spark-streaming #data-science

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Learn and master the most common data structures in this full course from Google engineer William Fiset. This course teaches data structures to beginners using high quality animations to represent the data structures visually.

You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding.

⭐️ Course Contents ⭐️

⌨️ (0:00:00) Abstract data types

⌨️ (0:04:28) Introduction to Big-O

⌨️ (0:17:00) Dynamic and Static Arrays

⌨️ (0:27:40) Dynamic Array Code

⌨️ (0:35:03) Linked Lists Introduction

⌨️ (0:49:16) Doubly Linked List Code

⌨️ (0:58:26) Stack Introduction

⌨️ (1:09:40) Stack Implementation

⌨️ (1:12:49) Stack Code

⌨️ (1:15:58) Queue Introduction

⌨️ (1:22:03) Queue Implementation

⌨️ (1:27:26) Queue Code

⌨️ (1:31:32) Priority Queue Introduction

⌨️ (1:44:16) Priority Queue Min Heaps and Max Heaps

⌨️ (1:49:55) Priority Queue Inserting Elements

⌨️ (1:59:27) Priority Queue Removing Elements

⌨️ (2:13:00) Priority Queue Code

⌨️ (2:28:26) Union Find Introduction

⌨️ (2:33:57) Union Find Kruskal’s Algorithm

⌨️ (2:40:04) Union Find - Union and Find Operations

⌨️ (2:50:30) Union Find Path Compression

⌨️ (2:56:37) Union Find Code

⌨️ (3:03:54) Binary Search Tree Introduction

⌨️ (3:15:57) Binary Search Tree Insertion

⌨️ (3:21:20) Binary Search Tree Removal

⌨️ (3:34:47) Binary Search Tree Traversals

⌨️ (3:46:17) Binary Search Tree Code

⌨️ (3:59:26) Hash table hash function

⌨️ (4:16:25) Hash table separate chaining

⌨️ (4:24:10) Hash table separate chaining source code

⌨️ (4:35:44) Hash table open addressing

⌨️ (4:46:36) Hash table linear probing

⌨️ (5:00:21) Hash table quadratic probing

⌨️ (5:09:32) Hash table double hashing

⌨️ (5:23:56) Hash table open addressing removing

⌨️ (5:31:02) Hash table open addressing code

⌨️ (5:45:36) Fenwick Tree range queries

⌨️ (5:58:46) Fenwick Tree point updates

⌨️ (6:03:09) Fenwick Tree construction

⌨️ (6:09:21) Fenwick tree source code

⌨️ (6:14:47) Suffix Array introduction

⌨️ (6:17:54) Longest Common Prefix (LCP) array

⌨️ (6:21:07) Suffix array finding unique substrings

⌨️ (6:25:36) Longest common substring problem suffix array

⌨️ (6:37:04) Longest common substring problem suffix array part 2

⌨️ (6:43:41) Longest Repeated Substring suffix array

⌨️ (6:48:13) Balanced binary search tree rotations

⌨️ (6:56:43) AVL tree insertion

⌨️ (7:05:42) AVL tree removals

⌨️ (7:14:12) AVL tree source code

⌨️ (7:30:49) Indexed Priority Queue | Data Structure

⌨️ (7:55:10) Indexed Priority Queue | Data Structure | Source Code

📺 The video in this post was made by freeCodeCamp.org

The origin of the article: https://www.youtube.com/watch?v=RBSGKlAvoiM&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#data structures #data structures easy to advanced course #google engineer #william fiset #data structures easy to advanced course - full tutorial from a google engineer #advanced course

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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!

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