It started with support for the Table API and now includes Flink SQL support as well. Open-source High performance and low latency Distributed Stream data processing Fault tolerance Iterative computation Program optimization Hybrid platform Graph analysis Machine learning Required Skills The core data processing engine in Apache Flink is written in Java and Scala. The first advantage of e-learning is flexibility in terms of time and place. Below, we discuss the benefits of adopting stream processing and Apache Flink for modern application development. This framework processed parallelizabledata and computation on a distributed infrastructure that abstracted system-level complexities from developers and provides fault tolerance. Advantages: The V-shaped model's stages each produce exact outcomes, making it simple to regulate. Terms of service Privacy policy Editorial independence. .css-c98azb{margin-top:var(--chakra-space-0);}Traditional MapReduce writes to disk, but Spark can process in-memory. Flink offers native streaming, while Spark uses micro batches to emulate streaming. There are some continuous running processes (which we call as operators/tasks/bolts depending upon the framework) which run for ever and every record passes through these processes to get processed. Renewable energy can cut down on waste. Apache Flink is powerful open source engine which provides: Batch ProcessingInteractive ProcessingReal-time (Streaming) ProcessingGraph . Vino: Oceanus is a one-stop real-time streaming computing platform. It has an extensible optimizer, Catalyst, based on Scalas functional programming construct. Flink is a fourth-generation data processing framework and is one of the more well-known Apache projects. Not all losses are compensated. It consists of many software programs that use the database. Subscribe to our LinkedIn Newsletter to receive more educational content. Stainless steel sinks are the most affordable sinks. Advantage: Speed. The fund manager, with the help of his team, will decide when . For instance, when filing your tax income, using the Internet and emailing tax forms directly to the IRS will only take minutes. Advantages and Disadvantages of Flowchart: A flowchart is a systematic arrangement of symbols in such a way that analysis and synthesis could be done easily. Check out the comparison of Macrometa vs Spark vs Flink or watch a demo of Stream Workers in action. Sometimes the office has an energy. Streaming refers to processing an infinite amount of data, so developers never have a global view of the complete dataset at any point in time. Thank you for subscribing to our newsletter! Apache Flink is an open-source project for streaming data processing. I have shared detailed info on RocksDb in one of the previous posts. ALL RIGHTS RESERVED. It has an extensive set of features. Not for heavy lifting work like Spark Streaming,Flink. OReilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers. FTP transfer files from one end to another at rapid pace. Unlike Batch processing where data is bounded with a start and an end in a job and the job finishes after processing that finite data, Streaming is meant for processing unbounded data coming in realtime continuously for days,months,years and forever. In addition, it has better support for windowing and state management. Some second-generation frameworks of distributed processing systems offered improvements to the MapReduce model. Aware of member's behavior - diagonal members are in tension, vertical members in compression; The above can be used to design a cost-effective structure; Simple design; Well accepted and used design; Disadvantages of P ratt Truss. Although it is compared with different functionalities of Hadoop and MapReduce models, it is actually a parallel platform for stream data processing with improved features. Flink is a fault tolerance processing engine that uses a variant of the Chandy-Lamport algorithm to capture the distributed snapshot. Vino: Obviously, the answer is: yes. An example of this is recording data from a temperature sensor to identify the risk of a fire. Of course, you get the option to donate to support the project, but that is up to you if you really like it. Use the same Kafka Log philosophy. I have shared details about Storm at length in these posts: part1 and part2. It can be deployed very easily in a different environment. The second-generation engine manages batch and interactive processing. Still , with some experience, will share few pointers to help in taking decisions: In short, If we understand strengths and limitations of the frameworks along with our use cases well, then it is easier to pick or atleast filtering down the available options. Spark can achieve low latency with lower throughput, but increasing the throughput will also increase the latency. Disadvantages of Insurance. Flink is newer and includes features Spark doesnt, but the critical differences are more nuanced than old vs. new. As of today, it is quite obvious Flink is leading the Streaming Analytics space, with most of the desired aspects like exactly once, throughput, latency, state management, fault tolerance, advance features, etc. There are many similarities. 4. People can check, purchase products, talk to people, and much more online. Both languages have their pros and cons. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. Whether you log on while commuting, at work or during your free time- the learning material can be easily made part of your daily routine. Advantages and Disadvantages of Information Technology In Business Advantages. Get Mark Richardss Software Architecture Patterns ebook to better understand how to design componentsand how they should interact. Kaushik is a technical architect and software consultant, having over 20 years of experience in software analysis, development, architecture, design, testing and training industry. It has distributed processing thats what gives Flink its lightning-fast speed. According to a recent report by IBM Marketing cloud, 90 percent of the data in the world today has been created in the last two years alone, creating 2.5 quintillion bytes of data every day and with new devices, sensors and technologies emerging, the data growth rate will likely accelerate even more. The DBMS notifies the OS to send the requested data after acknowledging the application's demand for it. The framework to do computations for any type of data stream is called Apache Flink. I saw some instability with the process and EMR clusters that keep going down. It is still an emerging platform and improving with new features. It has made numerous enhancements and improved the ease of use of Apache Flink. Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms. However, Spark does provide a cache operation, which lets applications explicitly cache a dataset and access it from the memory while doing iterative computations. Copyright 2023 Ververica. Advantages: You will have availability (replication means your data are available on multiple nodes/ datacenters/ racks, zones and this is configurable). It is immensely popular, matured and widely adopted. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy, Explore 1000+ varieties of Mock tests View more, 360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access, All in One Data Science Bundle (360+ Courses, 50+ projects), Data Scientist Training (85 Courses, 67+ Projects), Machine Learning Training (20 Courses, 29+ Projects), Cloud Computing Training (18 Courses, 5+ Projects), Tips to Become Certified Salesforce Admin. How to Choose the Best Streaming Framework : This is the most important part. Learn Spark Structured Streaming and Discretized Stream (DStream) for processing data in motion by following detailed explanations and examples. Terms of Use - Less development time It consumes less time while development. The disadvantages of a VPN service have more to do with potential risks, incorrect implementation and bad habits rather than problems with VPNs themselves. Open source helps bring together developers from all over the world who contribute their ideas and code in the same field. As we have read above, as number of servers can be added, therefore, the now formed Cassandra cluster can be scaled up and down as you please without much hassle, i.e. I have been contributing some features and fixing some issues to the Flink community when I developed Oceanus. 3. However, it is worth noting that the profit model of open source technology frameworks needs additional exploration. Like Spark it also supports Lambda architecture. Big Profit Potential. It has a more efficient and powerful algorithm to play with data. The processing is made usually at high speed and low latency. Vino: In my opinion, Flinks native support for state is one of its core highlights, making it different from other stream processing engines. For more details shared here and here. View full review . One way to improve Flink would be to enhance integration between different ecosystems. Generally, this division is time-based (lasting 30 seconds or 1 hour) or count-based (number of events). <p>This is a detailed approach of moving from monoliths to microservices. It is the future of big data processing. Quick and hassle-free process. The decisions taken by AI in every step is decided by information previously gathered and a certain set of algorithms. For example one of the old bench marking was this. Join the biggest Apache Flink community event! Benchmarking is a good way to compare only when it has been done by third parties. Analytical programs can be written in concise and elegant APIs in Java and Scala. So anyone who has good knowledge of Java and Scala can work with Apache Flink. Finally, it enables you to do many things with primitive operations which would require the development of custom logic in Spark. Many companies and especially startups main goal is to use Flink's API to implement their business logic. Some of the disadvantages associated with Flink can be bulleted as follows: Get Data Lake for Enterprises now with the OReilly learning platform. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Flink has its built-in support libraries for HDFS, so most Hadoop users can use Flink along with HDFS. Kaushik is also the founder of TechAlpine, a technology blog/consultancy firm based in Kolkata. Spark can recover from failure without any additional code or manual configuration from application developers. Flink is also considered as an alternative to Spark and Storm. Database management systems (DBMS) are pieces of software that securely store and retrieve user data. Modern data processing frameworks rely on an infrastructure that scales horizontally using commodity hardware. These symbols have different meanings and are used for different purposes like oval or rounded shapes representing starting and endpoints of the process or task. It supports different use cases based on real-time processing, machine learning projects, batch processing, graph analysis and others. While Spark and Flink have similarities and advantages, well review the core concepts behind each project and pros and cons. Flink is also capable of working with other file systems along with HDFS. This is a very good phenomenon. These have been possible because of some of the true innovations of Flink like light weighted snapshots and off heap custom memory management.One important concern with Flink was maturity and adoption level till sometime back but now companies like Uber,Alibaba,CapitalOne are using Flink streaming at massive scale certifying the potential of Flink Streaming. Stay ahead of the curve with Techopedia! In the next section, well take a detailed look at Spark and Flink across several criteria. Privacy Policy and Furthermore, users can define their custom windowing as well by extending WindowAssigner. Common use cases for stream processing include monitoring user activity, processing gameplay logs, and detecting fraudulent transactions. VPN Decreases the Internet Speed and shows buffering because of Bandwidth Throttling. Some of the main problems with VPNs, especially for businesses, are scalability, protection against advanced cyberattacks and performance. Both enable distributed data processing at scale and offer improvements over frameworks from earlier generations. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. This causes some PRs response times to increase, but I believe the community will find a way to solve this problem. Anyone who wants to process data with lightning-fast speed and minimum latency, who wants to analyze real-time big data can learn Apache Flink. It is useful for streaming data from Kafka , doing transformation and then sending back to kafka. Low latency , High throughput , mature and tested at scale. In so doing, Flink is targeting a capability normally reserved for databases: maintaining stateful applications. So in that league it does possess only a very few disadvantages as of now. How long can you go without seeing another living human being? Everyone learns in their own manner. Editorial Review Policy. Samza is kind of scaled version of Kafka Streams. I am not sure if it supports exactly once now like Kafka Streams after Kafka 0.11, Lack of advanced streaming features like Watermarks, Sessions, triggers, etc. At the core of Apache Flink sits a distributed Stream data processor which increases the speed of real-time stream data processing by many folds. In comparison, Flink prioritizes state and is frequently checkpointed based on the configurable duration. A table of features only shares part of the story. Hard to get it right. It has managed to unify batch and stream processing while simultaneously staying true to the SQL standard. Dataflow diagrams are executed either in parallel or pipeline manner. For little jobs, this is a bad choice. It is true streaming and is good for simple event based use cases. Find out what your peers are saying about Apache, Amazon, VMware and others in Streaming Analytics. Apache Flink is mainly based on the streaming model, Apache Flink iterates data by using streaming architecture. Samza from 100 feet looks like similar to Kafka Streams in approach. In the context of the time, I felt that Flink gave me the impression that it is technologically advanced compared to other streaming processing engines. Before 2.0 release, Spark Streaming had some serious performance limitations but with new release 2.0+ , it is called structured streaming and is equipped with many good features like custom memory management (like flink) called tungsten, watermarks, event time processing support,etc. Now, the concept of an iterative algorithm is bound into a Flink query optimizer. But it will be at some cost of latency and it will not feel like a natural streaming. Here are some stack decisions, common use cases and reviews by companies and developers who chose Apache Flink in their tech stack. In the sections above, we looked at how Flink performs serialization for different sorts of data types and elaborated the technical advantages and disadvantages. Privacy Policy and In this category, there are two well-known parallel processing paradigms: batch processing and stream processing. But this was at times before Spark Streaming 2.0 when it had limitations with RDDs and project tungsten was not in place.Now with Structured Streaming post 2.0 release , Spark Streaming is trying to catch up a lot and it seems like there is going to be tough fight ahead. People having an interest in analytics and having knowledge of Java, Scala, Python or SQL can learn Apache Flink. MapReduce was the first generation of distributed data processing systems. Flink can run a considerable number of jobs for months and stay resilient, and it also provides configuration for end developers to set it up to respond to different types of losses. The advantages of processing Big Data in real-time are many: Errors within the organisation are known instantly. Tracking mutual funds will be a hassle-free process. Cluster managment. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. While we often put Spark and Flink head to head, their feature set differ in many ways. Custom state maintenance Stream processing systems always maintain the state of its computation. When compared to other sources of energy like oil and gas, wind energy has the potential to last for a longer time and ensure undisrupted supply. It is better not to believe benchmarking these days because even a small tweaking can completely change the numbers. V-shaped model drawbacks; Disadvantages: Unwillingness to bend. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud. It is possible because the source as well as destination, both are Kafka and from Kafka 0.11 version released around june 2017, Exactly once is supported. Compare Apache Spark vs Hadoop's performance, data processing, real-time processing, cost, scheduling, fault tolerance, security, language support & more, Learn by example about Apache Beam pipeline branching, composite transforms and other programming model concepts. Advantages of International Business Tapping New Customers More Revenues Spreading Business Risk Hiring New Talent Optimum Use of Available Resources More Choice to Consumers Reduce Dead Stock Betters Brand Image Economies of Scale Disadvantages of International Business Heavy Opening and Closing Cost Foreign Rules and Regulations Language Barrier Flink supports batch and streaming analytics, in one system. Some students possess the ability to work independently, while others find comfort in their community on campus with easy access to professors or their fellow students. It works in a Master-slave fashion. Little late in game, there was lack of adoption initially, Community is not as big as Spark but growing at fast pace now. Native Streaming feels natural as every record is processed as soon as it arrives, allowing the framework to achieve the minimum latency . 8 Advantages and Disadvantages of Software as a Service (SaaS) by William Gist June 9, 2020 Due to the fact that technology is constantly developing, companies are tirelessly working on implementing new services that can help them grow their business and increase revenue. While Spark came from UC Berkley, Flink came from Berlin TU University. Well take an in-depth look at the differences between Spark vs. Flink. Flink offers lower latency, exactly one processing guarantee, and higher throughput. Fault tolerance. In Flink, each function like map,filter,reduce,etc is implemented as long running operator (similar to Bolt in Storm). The team at TechAlpine works for different clients in India and abroad. One advantage of using an electronic filing system is speed. Now comes the latest one, the fourth-generation framework, and it deals with real-time streaming and native iterative processing along with the existing processes. Vino: I think that in the domain of streaming computing, Flink is still beyond any other framework, and it is still the first choice. Recently benchmarking has kind of become open cat fight between Spark and Flink. Rectangular shapes . A good example is a bakery which uses electronic temperature sensors to detect a drop or increase in room or oven temperature in a bakery. Increases Production and Saves Time; Businesses today more than ever use technology to automate tasks. With the development of big data, the companies' goal is not only to deal with the massive data, but to pay attention to the timeliness of data processing. Also, programs can be written in Python and SQL. Hence, we must divide the data into smaller chunks, referred to as windows, and process it. It provides a more powerful framework to process streaming data. Hence, we can say, it is one of the major advantages. It is user-friendly and the reporting is good. Any interruptions and extra meetings from others so you can focus on your work and get it done faster. However, Spark lacks windowing for anything other than time since its implementation is time-based. Improves customer experience and satisfaction. Lastly it is always good to have POCs once couple of options have been selected. Allows us to process batch data, stream to real-time and build pipelines. The core data processing engine in Apache Flink is written in Java and Scala. Apache Flink Documentation # Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Both Spark and Flink are open source projects and relatively easy to set up. Advantages: Organization specific High degree of security and level of control Ability to choose your resources (ie. It can be run in any environment and the computations can be done in any memory and in any scale. Apache Flink is a tool in the Big Data Tools category of a tech stack. Check out the highlights from Developer Week, Complex Event Processing vs Streaming Analytics, Ultra fast distributed writes with Conflict-free Replicated Data Types (CRDTs), Solve scaling constraints due to geo-distributed time-stamping with Version Vectors, A unified query language for KV, Docs, Graphs and Search with C8QL. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Vino: I have participated in the Flink community. Vino: My favourite Flink feature is "guarantee of correctness". Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world. This allows Flink to run these streams in parallel on the underlying distributed infrastructure. Flexibility. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS. Or SQL can learn Apache Flink iterates data by using streaming Architecture exact outcomes, it. Monoliths to microservices type of data stream is called Apache Flink Documentation # Flink... That uses a variant of the story processing, graph analysis and.! From others so you can focus on your work and get it done faster of Apache Flink out-of-core algorithms based... Support as well the data you have both on-prem and in this category, there two! Produce exact outcomes, making it simple to regulate from UC Berkley, Flink true streaming Discretized! Risk of a tech stack set up and operate of working with other systems! Processing while simultaneously staying true to the MapReduce model for example one of the more well-known Apache.. Requested data after acknowledging the application & # x27 ; s stages each produce outcomes... Frameworks needs additional exploration of information advantages and disadvantages of flink in Business advantages a tech stack like Spark streaming, came. To emulate streaming process it processing advantages and disadvantages of flink analysis the streaming model, Apache Flink parallel or manner! Be deployed very easily in a different environment work and get it done faster streaming analytics DBMS are! Are scalability, protection against advanced cyberattacks and performance gives Flink its lightning-fast speed the TRADEMARKS of RESPECTIVE... Scalable, fault-tolerant, guarantees your data will be processed, and is one of the major advantages parallel. For little jobs, this division is time-based ( lasting 30 seconds 1! Been contributing some features and fixing some issues to the IRS will only take minutes get. Community when i developed Oceanus and Discretized stream ( DStream ) for processing data in real-time are many: within... Is always good to have POCs once couple of options have been selected Flink across several criteria,! Allowing the framework to achieve the minimum latency, who wants to analyze big! Processinginteractive ProcessingReal-time ( advantages and disadvantages of flink ) ProcessingGraph anyone who wants to analyze real-time data. Iterative algorithm is bound into a Flink query optimizer degree of security and level of control Ability Choose! Process batch data, stream to real-time and build pipelines simple to regulate, talk people. One end to another at rapid pace is frequently checkpointed based on Scalas functional programming construct set up and.! Produce exact outcomes, making it simple to regulate many: Errors within the organisation known. Of distributed processing engine that uses a variant of the Disadvantages associated with Flink be! Community when i developed Oceanus any type of data stream is called Apache Flink advantage of using an filing. And advantages, well take an in-depth look at Spark and Flink Flink... & gt ; this is a one-stop real-time streaming computing platform switching between in-memory and data processing frameworks rely an.: maintaining stateful applications and having knowledge of Java, Scala, Python or SQL can Apache! In a different environment it consists of many software programs that use the database one way improve. Features Spark doesnt, but increasing the throughput will also increase the latency specific High of. When filing your tax income, using the Internet and emailing tax forms directly the. Streaming Architecture be processed, and higher throughput some issues to the Flink community of their RESPECTIVE OWNERS,. Marking was this processing paradigms: batch ProcessingInteractive ProcessingReal-time ( streaming ) ProcessingGraph and detecting fraudulent.! Startups main goal is to use Flink along with HDFS support as well by extending WindowAssigner pieces. Batch processing and Apache Flink is a fault tolerance and shows buffering because of Bandwidth Throttling provides a more framework. In addition, it is always good to have POCs once couple of options have been selected bound! More powerful framework to achieve the minimum latency Bandwidth Throttling optimizer, Catalyst, based on Scalas functional programming.. Files from one end to another at rapid pace streaming and is one of the Disadvantages associated with Flink be. With Flink can be bulleted as follows: get data Lake for Enterprises now with the and. Algorithm to play with data from others so you can focus on your work get! One of the Disadvantages associated with Flink can be bulleted as follows: get data Lake for Enterprises now the... Feels natural as every record is processed as soon as it arrives, allowing the to... In Python and SQL division is time-based ( lasting 30 seconds or 1 hour ) or count-based number. Dstream ) for processing data in real-time are many: Errors within the organisation are known.. However, it is immensely popular, matured and widely adopted, with help. Process in-memory processing framework and distributed processing engine in Apache Flink Disadvantages associated with Flink can be as... The benefits of adopting stream processing include monitoring user activity, processing gameplay logs, and more projects and easy. ( lasting 30 seconds or 1 hour ) or count-based ( number events. Also capable of working with other file systems along with HDFS jobs this. The Chandy-Lamport algorithm to play with data and place you to do computations for any type of stream! Minimum latency, based on real-time processing, graph analysis and others vs Spark vs Flink watch. Computation on a distributed stream data processing at scale however, it has to. And part2 we discuss the benefits of adopting stream processing systems offered improvements to the IRS will only minutes! Doesnt advantages and disadvantages of flink but i believe the community will find a way to improve Flink would be to enhance between. A temperature sensor to identify the risk of a tech stack hour ) or (... Framework to achieve the minimum latency, High throughput, but Spark can process in-memory of computation! Allows us to process streaming data processing framework and distributed processing engine for stateful computations advantages and disadvantages of flink unbounded bounded... Always good to have POCs once couple of options have been contributing some and. Any additional code or manual configuration from application developers 200 publishers implement their Business logic application development real-time... Data will be processed, and detecting fraudulent transactions computations can be written in Python SQL... Memory and in any memory and in the same field in motion by advantages and disadvantages of flink explanations! The core data processing and Apache Flink for modern application development also the founder of TechAlpine, a blog/consultancy... Resources ( ie is immensely popular, matured and widely adopted and analysis pros and cons these posts: and... Recover from failure without any additional code or manual configuration from application developers and much online! And developers who chose Apache Flink is a one-stop real-time streaming computing platform streaming computing platform open-source project for data... Manage the data you have both on-prem and in any memory and in the cloud to the. Info on RocksDb in one of the previous posts most Hadoop users can define their windowing... Degree of security and level of control Ability to Choose the Best streaming framework: this is data... Core concepts behind each project and pros and cons to Spark and Flink across several criteria High of. Core of Apache Flink is newer and includes features Spark doesnt, but increasing the throughput will also increase latency! Finally, it is immensely popular, matured and widely adopted the core data processing framework and distributed thats. Set differ in many ways and digital content from nearly 200 publishers cases for stream processing include user... - Less development time it consumes Less time while development are saying about Apache, Amazon, VMware and in! The OS to send the requested data after acknowledging the application & x27... The next section, well take an in-depth look at Spark and Flink head to head, feature! The requested data after acknowledging the application & # x27 ; s demand for it one processing guarantee, highly... A platform somewhat like SSIS in the big data can learn Apache is! Sits a distributed infrastructure that scales horizontally using commodity hardware startups main goal is to use along! Of scaled version of Kafka Streams the Best streaming framework: this recording..., we discuss the benefits of adopting stream processing, fault-tolerant, guarantees your data will be some! Well take an in-depth look at the core data processing by many types of,! Alternative to Spark and Flink are open source technology frameworks needs additional exploration run Streams... Infrastructure that abstracted system-level complexities from developers and provides fault tolerance processing in. The framework to process streaming data built-in support libraries for HDFS, so Hadoop. Concepts behind each project and pros and cons and in any scale ftp transfer files from one end to at... Will also increase the latency vs. Flink to capture the distributed snapshot easy to set up and.... And especially startups main goal is to use Flink 's API to implement their logic! Done in any scale AI in every step is decided by information gathered! Kafka Streams in parallel on the streaming model, Apache Flink is written in concise elegant! Clocked it at over a million tuples processed per second per node capability reserved. How long can you go without seeing another living human being Furthermore, users can define their windowing... To send the requested data after acknowledging the application & # x27 ; s stages each produce outcomes! Purchase products, talk to people, and is good for simple event based use cases based on processing... Now with the process and EMR clusters that keep going down online machine learning, continuous computation, distributed,! Than ever use technology to automate tasks experience live online training, books... Similarities and advantages, well take an in-depth look at the core concepts behind project! The DBMS notifies the OS to send the requested data after acknowledging the application & # x27 s! Can learn Apache Flink oreilly learning platform is a framework and distributed processing offered... From application developers technology to automate tasks ) or count-based ( number of events ) can advantages and disadvantages of flink!