Spark Structured Streaming – Handling Late Data

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

Handling Late Data

With window aggregates (discussed in the previous blog) Spark automatically takes cares of late data. Every aggregate window is like a bucket i.e. as soon as we receive data for a particular new time window, we automatically open up a bucket and start counting the number of records falling in that bucket. These buckets stay open for data which may even come 5 hours late and it will still update that old bucket and thus incrementing the count.

#analytics #apache spark #ml #ai and data engineering #scala #spark #tech blogs #structured streaming #watermark

What is GEEK

Buddha Community

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

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Spark Structured Streaming – Handling Late Data

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

Handling Late Data

With window aggregates (discussed in the previous blog) Spark automatically takes cares of late data. Every aggregate window is like a bucket i.e. as soon as we receive data for a particular new time window, we automatically open up a bucket and start counting the number of records falling in that bucket. These buckets stay open for data which may even come 5 hours late and it will still update that old bucket and thus incrementing the count.

#analytics #apache spark #ml #ai and data engineering #scala #spark #tech blogs #structured streaming #watermark

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

---

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

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