Apache Flink: What, How, Why, Who, Where?
Slim Baltagi
I help with Snowflake AI Data Cloud Cost & Performance Optimization, and Administration
On February 2nd, 2016, I gave a talk titled "Apache Flink: What, How, Why, Who, Where?" at the New York City (NYC) Apache Flink Meetup that I founded on December 23, 2015. The event, that took place at the NYC Civic Hall, was sponsored by Bloomberg and Capital One.
This is the video recording
and the slides of my talk.
This introductory level talk was about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source.
With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing.
In this talk, you will learn about:
1. What is Apache Flink stack and how it fits into the Big Data ecosystem?
2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment?
3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark.
4. Who is using Apache Flink?
5. Where to learn more about Apache Flink?
You comments, here or on the web sites hosting the video recording and the slides of my talk, are much appreciated.
Big Data & Cloud Technical Architect(now more of advisory role )
9 年Spark's micro-batching is an issue for fast ultra-low latency real-time data reqt. But it is accepted because of strong marketing lobby. As a result it gains momentum and it has more contributors, adoption... Storm subsides slowly because of igniting Spark.Now we need to see Flink.Proprietary products like Streambase, Apama, IBM Infosphere streams have seen the drawbacks of Open source. They have strong integration IDE coupled with rich libraries to connect to assorted sources, DB, market data, streaming analytics, statistics application,live data, operational BI... Some have specific ultra-low latency application servers like in-memory though not in-chip( which may come up maybe). The rate of changes in open source releases seems to be scary and difficult to catch up. Deprecated features need to be amended most of the time :)
Chief Data Officer - FSS DTT@ IBM | Data and Advanced Analytics | MIT Data Science
9 年Great Post Slim..
Reimagining contact center as a hands-on architect bridging users, clients, developers, and business executives in their context.
9 年The best information