Danica Fine's guest appearances

Danica Fine

Hanselminutes with Scott Hanselman

Streaming Audio: Apache Kafka® & Real-Time Data

Alexa's Input (AI) 

OwlTail

Danica Fine is a Senior Developer Advocate at Confluent. She is a big fan of the power of data and has deep expertise in Apache Kafka. She chats with Scott about the importance of a strongly architected data platform and gives tips on when you need to move from the basics of SQL to a true data rich environment that includes data streaming products.Head over to https://elevateai.com/hanselminutes to sign up today and get started!

Danica Fine is a Senior Developer Advocate at Confluent. She is a big fan of the power of data and has deep expertise in Apache Kafka. She chats with Scott about the importance of a strongly architected data platform and gives tips on when you need to move from the basics of SQL to a true data rich environment that includes data streaming products.<ul><li>Head over to <a href="https://elevateai.com/hanselminutes " rel="nofollow" target="_blank">https://elevateai.com/hanselminutes </a>to sign up today and get started!</li></ul>

Towards a better Data Platform with Danica Fine

Danica Fine, Senior Developer Advocate at Confluent, joins this episode to talk about how Confluent provides tools for creating a streaming data platform. We discuss what Confluent is and the problems it solves, Confluent Cloud, Kafka, some interesting use cases and much more!  Danica graduated with a B.A. in Logic, Information, and Computation and a Masters of Science in Engineering in Scientific Computing from the University of Pennsylvania. Danica worked as a software engineer for almost 5 years at Bloomberg, where she worked on streaming infrastructure. Danica has been working at Confluent as a Senior Developer Advocate for a little over one year. Links: Danica's twitter, Confluent's YouTube channel, Confluent's engineering blog You can support this podcast on the anchor page. Make sure to subscribe and follow Alexa's Input Twitter account to get notified when a new podcast episode comes out.  ---   Support this podcast: https://podcasters.spotify.com/pod/show/alexagriffith/support

<a href="//linkedin.com/in/danica-fine/" rel="nofollow" target="_blank">Danica Fine</a>, Senior Developer Advocate at <a href="https://www.confluent.io/" rel="nofollow" target="_blank">Confluent</a>, joins this episode to talk about how Confluent provides tools for creating a streaming data platform. We discuss what Confluent is and the problems it solves, Confluent Cloud, <a href="https://kafka.apache.org/" rel="nofollow" target="_blank">Kafka</a>, some interesting use cases and much more!  Danica graduated with a B.A. in Logic, Information, and Computation and a Masters of Science in Engineering in Scientific Computing from the University of Pennsylvania. Danica worked as a software engineer for almost 5 years at Bloomberg, where she worked on streaming infrastructure. Danica has been working at Confluent as a Senior Developer Advocate for a little over one year. Links: Danica's <a href="https://twitter.com/TheDanicaFine?s=20&amp;t=JsEIQlPrJF2OZoTFlNcVpA" rel="nofollow" target="_blank">twitter</a>, Confluent's <a href="//youtube.com/c/Confluent" rel="nofollow" target="_blank">YouTube channel</a>, Confluent's <a href="https://www.confluent.io/blog/?utm_medium=sem&amp;utm_source=google&amp;utm_campaign=ch.sem_br.brand_tp.prs_tgt.confluent-brand_mt.mbm_rgn.namer_lng.eng_dv.all_con.confluent-blog&amp;utm_term=%2Bconfluent%20%2Bblog&amp;creative=&amp;device=c&amp;placement=&amp;gclid=Cj0KCQjwjvaYBhDlARIsAO8PkE2jRZf0JkazyJmz9uIk18Vld3z4dAEUXE5h6gWuJJ_enn7jVjgZYUYaAt54EALw_wcB" rel="nofollow" target="_blank">engineering blog</a> You can support this podcast on the <a href="https://anchor.fm/alexagriffith" rel="nofollow" target="_blank">anchor page</a>. Make sure to subscribe and follow <a href="https://twitter.com/alexasinput" rel="nofollow" target="_blank">Alexa's Input Twitter account</a> to get notified when a new podcast episode comes out. --- Support this podcast: <a href="https://podcasters.spotify.com/pod/show/alexagriffith/support" rel="payment nofollow" target="_blank">https://podcasters.spotify.com/pod/show/alexagriffith/support</a>

Kafka Streaming and Confluent with Danica Fine

Implementing an event-driven data pipeline can be challenging, but doing so within the context of a legacy architecture is even more complex. Having spent three years building a streaming data infrastructure and being on the first team at a financial organization to implement Apache Kafka® event-driven data pipelines, Danica Fine (Senior Developer Advocate, Confluent) shares about the development process and how ksqlDB and Kafka Connect became instrumental to the implementation.By moving away from batch processing to streaming data pipelines with Kafka, data can be distributed with increased data scalability and resiliency. Kafka decouples the source from the target systems, so you can react to data as it changes while ensuring accurate data in the target system. In order to transition from monolithic micro-batching applications to real-time microservices that can integrate with a legacy system that has been around for decades, Danica and her team started developing Kafka connectors to connect to various sources and target systems. Kafka connectors: Building two major connectors for the data pipeline, including a source connector to connect the legacy data source to stream data into Kafka, and another target connector to pipe data from Kafka back into the legacy architecture. Algorithm: Implementing Kafka Streams applications to migrate data from a monolithic architecture to a stream processing architecture. Data join: Leveraging Kafka Connect and the JDBC source connector to bring in all data streams to complete the pipeline.Streams join: Using ksqlDB to join streams—the legacy data system continues to produce streams while the Kafka data pipeline is another stream of data. As a final tip, Danica suggests breaking algorithms into process steps. She also describes how her experience relates to the data pipelines course on Confluent Developer and encourages anyone who is interested in learning more to check it out. EPISODE LINKSData Pipelines courseIntroduction to Streaming Data Pipelines with Apache Kafka and ksqlDBGuided Exercise on Building Streaming Data PipelinesMigrating from a Legacy System to Kafka StreamsWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Implementing an event-driven data pipeline can be challenging, but doing so within the context of a legacy architecture is even more complex. Having spent three years building a streaming data infrastructure and being on the first team at a financial organization to implement Apache Kafka® event-driven data pipelines, Danica Fine (Senior Developer Advocate, Confluent) shares about the development process and how ksqlDB and Kafka Connect became instrumental to the implementation.By moving away from batch processing to streaming data pipelines with Kafka, data can be distributed with increased data scalability and resiliency. Kafka decouples the source from the target systems, so you can react to data as it changes while ensuring accurate data in the target system. In order to transition from monolithic micro-batching applications to real-time microservices that can integrate with a legacy system that has been around for decades, Danica and her team started developing Kafka connectors to connect to various sources and target systems. <ol><li>Kafka connectors: Building two major connectors for the data pipeline, including a source connector to connect the legacy data source to stream data into Kafka, and another target connector to pipe data from Kafka back into the legacy architecture. </li><li>Algorithm: Implementing Kafka Streams applications to migrate data from a monolithic architecture to a stream processing architecture. </li><li>Data join: Leveraging Kafka Connect and the JDBC source connector to bring in all data streams to complete the pipeline.</li><li>Streams join: Using ksqlDB to join streams—the legacy data system continues to produce streams while the Kafka data pipeline is another stream of data. </li></ol>As a final tip, Danica suggests breaking algorithms into process steps. She also describes how her experience relates to the data pipelines course on Confluent Developer and encourages anyone who is interested in learning more to check it out. EPISODE LINKS<ul><li><a href="https://developer.confluent.io/learn-kafka/data-pipelines/intro/" rel="nofollow" target="_blank">Data Pipelines course</a></li><li><a href="https://www.confluent.io/blog/streaming-data-pipeline-with-apache-kafka-and-ksqldb/" rel="nofollow" target="_blank">Introduction to Streaming Data Pipelines with Apache Kafka and ksqlDB</a></li><li><a href="https://www.meetup.com/KafkaBayArea/events/283207862/?isFirstPublish=true" rel="nofollow" target="_blank">Guided Exercise on Building Streaming Data Pipelines</a></li><li><a href="https://videos.confluent.io/watch/KCpQFZQzL1dtJCQJMRaKjz" rel="nofollow" target="_blank">Migrating from a Legacy System to Kafka Streams</a></li><li><a href="https://www.youtube.com/watch?v=vL_79dmF_8s&amp;list=PLa7VYi0yPIH1B0i7mhzVi78TIkKSd-0vE&amp;index=37" rel="nofollow" target="_blank">Watch the video version of this podcast</a></li><li><a href="https://www.confluent.io/community/ask-the-community/" rel="nofollow" target="_blank">Join the Confluent Community</a></li><li><a href="https://developer.confluent.io/" rel="nofollow" target="_blank">Learn more with Kafka tutorials, resources, and guides at Confluent Developer</a></li><li><a href="https://www.confluent.io/resources/online-talk/demo-intro-to-event-driven-microservices-with-confluent/" rel="nofollow" target="_blank">Live demo: Intro to Event-Driven Microservices with Confluent</a></li><li><a href="https://www.confluent.io/confluent-cloud/tryfree" rel="nofollow" target="_blank">Use PODCAST100 to get an additional $100 of free Confluent Cloud usage</a> (<a href="https://www.confluent.io/confluent-cloud-promo-disclaimer/" rel="nofollow" target="_blank">details</a>)</li></ul>