The term ‘Big Data’ contains more than just its reference to quantity and volume. Living in the era of readily available information and instantaneous communication, it is not surprising that data architectures have been shifting to be stream-oriented. Real value for companies doesn’t just come from sitting on a gargantuan amount of collected data points, but also from their ability to extract actionable insights as quickly as possible (even in real-time). Processing data at faster rates allows a company to react to changing business conditions in real-time.
It goes without saying that over the last decade, there has been a constant growth for applications (aka message-broker software) capable of capturing, retain and process this overwhelmingly rapid flow of information. As of 2020, Apache Kafka is one of the most widely adopted message-broker software (used by the likes of Netflix, Uber, Airbnb and LinkedIn) to accomplish these tasks. This blog will give a very brief overview of the concept of stream-processing, streaming data architecture and why Apache Kafka has gained so much momentum.
Stream-processing is best visualised as a river. In the same way that water flows through a river, so do packages of information in the endless flow of stream-processing.
According to AWS, the general definition of streaming data would be “data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes)”.
Data streaming works particularly well in time-series in order to find underlying patterns over time. It also really shines in the IoT space where different data signals can be constantly collected. Other common uses are found on web interactions, e-commerce, transaction logs, geolocation points and much much more.
There is a subtle difference between real-time processing and stream-processing:
#kafka-streams #data-science #apache
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 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
Corona Virus Pandemic has brought the world to a standstill.
Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.
Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!
And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!
If you are still not aware of it, don’t worry as Georgia Byng has well said,
“No time is better than the present”
– Georgia Byng, a British children’s writer, illustrator, actress and film producer.
No matter if you are a developer (be it front-end or back-end) or a data scientist, tester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.
From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.
Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.
These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.
Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).
xlr8rs make your development real fast by just adding your core business logic to the template.
If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂
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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
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Apache Flink offers rich sources of API and operators which makes Flink application developers productive in terms of dealing with the** multiple data streams**. Flink provides many multi streams operations like Union, Join, and so on. In this blog, we will explore the Window Join operator in Flink with an example. It joins two data streams on a given key and a common window.
Let say we have one stream which contains salary information of all the individual who belongs to an organization. The salary information has the id, name, and salary of an individual. This stream is available at port 9000 on the localhost.
#apache flink #big data and fast data #flink #java ##apache flink #big #big data analytics #fast data analytics #flink streaming #joins #streaming #streaming 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.
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