Java Pub House

Allright, it is time to pull the curtain on all this AI stuff and really learn how it works! On this episode we dive deep into AI, and Neural Networks, refinenements, vector databases (and why we need them) so you can understand the underlying principles of AI and LLM! The field is so vast, intersting and more importantly it's going to be here to stay. So take a listen and keep learning on this new tool we should all be familiar with! http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/  - https://ollama.com/library/llama3.1/blobs/f1cd752815fc  - https://onnx.ai/  - https://partee.io/2022/08/11/vector-embeddings/  - https://codelabs.milvus.io/vector-database-101-introduction-to-unstructured-data  - https://www.guru99.com/backpropogation-neural-network.html  - https://deeplearning4j.konduit.ai/  - https://spring.io/projects/spring-ai  - https://medium.com/data-science-at-microsoft/how-large-language-models-work-91c362f5b78f  - https://www.v7labs.com/blog/neural-network-architectures-guide Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us!  https://www.twitter.com/javapubhouse

So we continue to have guests in our show to talk to us about interesting things... This time is about Apache Tika. This is an incredible tool to do search file processing and metadata extraction. Think about that you have tons of unstructured files, like emails, or documents, and you want to extract, index and then search theses. This is Tika's purpose. And who best to walk us through how it does its magic that its Project Management Committee (PMC) Chair, Tim Allison! So take a listen as we go deeper on ingesting tons of content (which is fundamental for things like training LLMs). http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ Apache Tika * https://tika.apache.org/ OpenSearch Project and OpenSearch Neural Plugin Tutorials * https://opensearch.org/ * https://opensearch.org/docs/latest/search-plugins/neural-search/ * https://opster.com/guides/opensearch/opensearch-machine-learning/how-to-set-up-vector-search-in-opensearch/  * https://opster.com/guides/opensearch/opensearch-machine-learning/opensearch-hybrid-search/ * https://sease.io/2024/01/opensearch-knn-plugin-tutorial.html * https://sease.io/2024/04/opensearch-neural-search-tutorial-hybrid-search.html Selected Advanced File Processing toolkits/services * https://unstructured.io/ * https://aws.amazon.com/textract/ * https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence Selected Hybrid Search/RAG toolkits (there are _MANY_ others!) * Haystack: https://haystack.deepset.ai/ * LangChain: https://www.langchain.com/ * LangStream: https://langstream.ai/ Search/Relevance Conferences * https://haystackconf.com/ * https://2024.berlinbuzzwords.de/ * https://mices.co/ Tim's personal project * JavaFX (ahem) tika-config writer UI: https://github.com/tballison/tika-gui-v2 Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us!  https://www.twitter.com/javapubhouse

We have a great time talking to Matt Topol from Voltron Data on one of his Apache Software Foundation projects called Apache Arrow. It's both a spec and implementation of a columnar data format that is not only efficient, but cross-language compatible. We walk through the scenarios that it covers and how is becoming more and more pivotal for things like ML and LLMs. So come listen to this JPH episode on one of the best and free ways to distribute data and integrate services working on top of that data! http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/  - Apache Arrow Project (https://arrow.apache.org/)  - Java implementation (https://arrow.apache.org/docs/java/index.html)  - In-Memory Analytics with Apache Arrow (https://www.oreilly.com/library/view/in-memory-analytics-with/9781801071031/)  - Matt Topol X (Twitter!) Account (https://twitter.com/zeroshade)  -  Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us!  https://www.twitter.com/javapubhouse

Ok, so it's an incredible time to be in the Java Ecosystem, and one of the biggest frameworks out there just dropped their three-point-oh version! That's right! So Spring Boot is not officially 3.0, and it has as a Baseline Java 17! (oohh!!). So we brought in the big guns to talk about what does it mean to Upgrade to Spring Boot 3, and what are the new cool toys we can expect from that upgrade!  In all, an amazing interview full of great things that are available NOW (so whatcha waiting for! Upgrade!) http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/  - Dan Vega's own What's new in Spring Boot 3 (https://www.danvega.dev/newsletter/whats-new-spring-boot-3/)  - Official Spring Framework 6 Wiki (https://github.com/spring-projects/spring-framework/wiki/What's-New-in-Spring-Framework-6.x)  - Spring Boot 3, and Spring Framework 6, What's new? (https://www.baeldung.com/spring-boot-3-spring-6-new)  - Spring Boot 3 goes GA (https://spring.io/blog/2022/11/24/spring-boot-3-0-goes-ga)  - Preparing for Spring Boot 3 (https://spring.io/blog/2022/05/24/preparing-for-spring-boot-3-0)  -  Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us!  https://www.twitter.com/javapubhouse

Whew! So we took a big break over summer (like Bob said, we were just swamped with work.. oof), but we are BACK! and like always we are ready to explore even deeper Java topics for the professional developer. This time we set our sights in Apache Kafka, one of the (if not THE) dominant distributed messaging framework / broker. If you have been integrating webservices, you might have been running into message brokers (and applying Enterprise Integration Patterns), well if so, you most likely have run into Kafka.  We dive into "What does Kafka Solve", into what it is (and isn't), and why you should use it (or not use it), and how it differs from traditionalling Messaging systems. In all, this is another episode of "Cloud stuff", and, like you know, that's where everything interesting is at! So have a listen!  http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ Apache Kafka https://kafka.apache.org/ Kafka Quick Start https://developer.confluent.io/quickstart/kafka-on-confluent-cloud/ What IS Apache Kafka https://developer.confluent.io/what-is-apache-kafka/ Apache Kafka Quickstart (With Tim Berglund, Hi!!!) https://kafka.apache.org/quickstart  Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us!  https://www.twitter.com/javapubhouse  

Yeah! so we have been working with the cloud for a while, terms like K8s, EC2, Route53, BlobData, CLI has been swinged around... and is a little mysterious, and sometimes looks like a black box where you just click buttons, add things, type commands, until it finally something good happens. But if something bad happens, we tend not to have the slightest idea on why! Never fear, on this episode of PubHouse we start from the very beginning on describing (And dismantling) what the "Cloud" really is... Starting on how we got there, what are the different "components" of most clouds, and how to reason about them. This way, the next time something unpredictable happens, you will know exactly where to start troubleshooting and how to navigate this space! This is the first episode of a series (probably a series of 2) on Cloud technologies. So if you never been exposed to the cloud and you're curious, or even if you've been working on it but everything sounds mysterious, then dive in! http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ Intro to AWS  https://docs.aws.amazon.com/whitepapers/latest/aws-overview/introduction.html What is Elastic Load Balancing https://docs.aws.amazon.com/elasticloadbalancing/latest/userguide/what-is-load-balancing.html What is Amazon Route 53 https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/Welcome.html EC2 vs ECS vs Lambda https://www.cloudzero.com/blog/ecs-vs-ec2 Intro to Azure Fundamentals https://docs.microsoft.com/en-us/learn/modules/intro-to-azure-fundamentals/ Azure Containter Registry https://azure.microsoft.com/en-us/services/container-registry/#features Azure Compute https://azure.microsoft.com/en-us/services/virtual-machines/#overview Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us!  https://www.twitter.com/javapubhouse  

Ok, so is time to talk about something secretive! Like API Passwords, Auth tokens, or keys... these are things that we want to have as a Secret within our microservice. And yeah, adding them into your source code is a big no-no Here we cover the dos (and dont's) of secret management, what are the benefits and drawbacks of the different solutions and we explore some of our favorite open source (and Cloud tools) for keeping secrets. We answer some important questions on how to effectively store and manage these secrets (the short answer is don't try to do it yourself!), and end up with the list of best practices for it. If you are building a non-trivial (or interesting) web service, this is a must-listen episode! http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ Language Features Using AWS Secrets Manager to manage secrets in Spring Boot https://raymondhlee.wordpress.com/2019/10/11/using-aws-secrets-manager-to-manage-secrets-in-spring-boot-applications/ AWS Secrets Manager https://aws.amazon.com/secrets-manager/ Spring Cloud AWS https://cloud.spring.io/spring-cloud-aws/reference/html/ Hashicorp Vault https://www.vaultproject.io/ Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us! https://www.twitter.com/javapubhouse  

So is time to celebrate! We got a new box of toys with the new release of Java! This is also a Long-Term-Support release which means that's usually a "good one" to jump into! Switch Expressions! Helpful Nullpointers, Sealed Classes... there is a TON that's new And we got the best Doctor in town to walk us through all of them. We're of course talking about Stuart Marks! (AKA Dr. Deprecator). We cover most of the important features from 11 to 17 (there's a ton that was left out, so keep following the links to know more!) http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ Language Features Pattern Matching for instanceof https://docs.oracle.com/en/java/javase/16/language/pattern-matching-instanceof-operator.html Switch Expressions https://docs.oracle.com/en/java/javase/13/language/switch-expressions.html Sealed Classes https://docs.oracle.com/en/java/javase/15/language/sealed-classes-and-interfaces.html Text Blocks https://docs.oracle.com/en/java/javase/13/text_blocks/index.html Debugging Features Helpful NullPointerExceptions https://www.baeldung.com/java-14-nullpointerexception Performance Features New Garbage Collectors (Shenandoah, ZGC) https://blogs.oracle.com/javamagazine/post/understanding-the-jdks-new-superfast-garbage-collectors Unix-Domain Socket Channels https://openjdk.java.net/jeps/380 Deprecation & Platform evolution Remove the Nashorn JavaScript Engine (Plug Graal!) https://openjdk.java.net/jeps/372 Deprecate the Security Manager for Removal and Applets https://openjdk.java.net/jeps/411 Strongly Encapsulate JDK Internals https://openjdk.java.net/jeps/403 Warnings for Value-Based Classes https://openjdk.java.net/jeps/390 Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us! https://www.twitter.com/javapubhouse  

So while Java is the "main" language of the JVM, it is by no means the "only" language. And one of the purely functional programming languages is getting a new facelift! Scala has been going through a revamp on the syntax and the features, and if you ever run into a scala user you know how passionate they are about their language! (hint: they love it!) So while we might be working in Java projects most of our time, it is a good time to revisit that developer toolbox and learn when and where to use it. And if anything else, having a dip into a fully functional programming language teaches any budding developer how to see things differently! So maybe Scala is "not" the language you want to program all your new projects, but understanding its strengs, its thinking (and with Scala 3) and its purpose, it makes it for a very sharp tool to have at the ready when you really need to tackle that Data Sciency / Functional Programming problem. In addition, we bring one of the biggest names in the Scala Community to walk us through it all @DeanWampler (https://twitter.com/deanwampler) has been working with Scala since the early days and has just published his new O'Reilly book Programming Scala (https://deanwampler.github.io/books/programmingscala.html). So what are you waiting for, add a new tool to your toolbox with Scala 3.0! http://www.javapubhouse.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ Programming Scala (Dean Wampler) https://deanwampler.github.io/books/programmingscala.html New In Scala 3 https://docs.scala-lang.org/scala3/new-in-scala3.html Scala 3 Features https://docs.scala-lang.org/scala3/book/scala-features.html A look at inline https://medium.com/scala-3/scala-3-a-look-at-inline-and-programming-scala-is-now-published-9690ca43c23a Safer Pattern Matching with Matchable https://medium.com/scala-3/scala-3-safer-pattern-matching-with-matchable-f0396430ded6 Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us! https://www.twitter.com/javapubhouse  

You'll run it everywhere there's a production system. Some kind of Metrics collector like Statsd or InfluxDB. They work like magic, reporting on the health of your application. but how does it really happen? We dive in on how these Application Performance Management tools work and how do they really "see into" your application. We demistify all this magic, and actually describe how to add "new" metrics and how to publish it! If you ever worked in a production application, and were wondering how do these metrics get graphed and collected, well, wonder-no-more! We go deep into Micrometer (the SLF4J of Metrics) and how it all works! http://www.javaoffheap.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ Micrometer https://micrometer.io/ Creating Meters https://micrometer.io/docs/concepts#_meters Spring Boot and Micrometer https://spring.io/blog/2018/03/16/micrometer-spring-boot-2-s-new-application-metrics-collector Statsd https://github.com/statsd/statsd Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us! https://www.twitter.com/javapubhouse