GigaSpaces PAAS For Cloud Based Java Applications

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Session presented at the 2nd IndicThreads.com Conference on Cloud Computing held in Pune, India on 3-4 June 2011. http://CloudComputing.IndicThreads.com

Technology Education

Gigapaces PAAS For Cloud Based Java Applications Karan Nangru Xebia IT Architects

What is GigaSpaces XAP? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Tier Based Architecture Tier integration impacts scalability, performance, and reliability

The Gigaspaces Architecture A single model for full life cycle enterprise systems

From J2EE to XAP Component J2EE XAP Persistency JPA Hibernate, JPA Messaging JMS,MDB JMS, Polling/Notify Container , Native Messaging API Security JAAS, SSL Spring Security, SSL Web Session Management HttpSession HttpSession (via Jetty) Transaction Management JTA Spring Transaction via Jini Transaction Manager Data Access JDBC , Session Bean, Entity Bean JDBC, Space, JPA Remoting EJB, IIOP, RMI Spring remoting over LRMI Web Servlet, JSP Servlet, JSP (via Jetty) Packaging and deployment ear , war jar , war Contexts and Dependency Injection JSR 299 Spring IOC System Management JMX JMX , SNMP, Native Admin API Java Naming and Directory Service JNDI Jini Lookup Service

What Else…. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Scope of this session….. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Space Based Architecture – Scaling Diagram 1. Car wash, the tier based way: a different warehouse for each step in the process.

Space Based Architecture – The Unit System Diagram 2. An all-in-one "washing unit" containing soaping, watering and drying services.

Space Based Architecture – Multiple Units Diagram 3. Scaling the system by adding more "car washing units"

Space Based Architecture – A GigaSpaces Processing Unit Diagram 4. A Processing Unit with 3 services collocated with a space.

Web application Deployment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Remoting ,[object Object],[object Object],The First phase involves the Service invocation The Second phase involves reducing the results retrieved from the Services

Messaging and Processing - Polling Container ,[object Object],[object Object],Write

Messaging and Processing – Notify Container ,[object Object],[object Object],Write

Several different frameworks have been developed to draw data from Kafka and maintain standard SQL over continually changing data. This provides an easy way to query and transform data - now accessible by orders of magnitude more users. At the same time, using Standard SQL against changing data is a new pattern for many engineers and analysts. While the language hasn’t changed, we’re still in the early stages of understanding the power of SQL over Kafka - and in some interesting ways, this new pattern introduces some exciting new idioms. In this session, we’ll start with some basic use cases of how Standard SQL can be effectively used over events in Kafka- including how these SQL engines can help teams that are brand new to streaming data get started. From there, we’ll cover a series of more advanced functions and their implications, including: - WHERE clauses that contain time change the validity intervals of your data; you can programmatically introduce and retract records based on their payloads! - LATERAL joins turn streams of query arguments into query results; they will automatically share their query plans and resources! - GROUP BY aggregations can be applied to ever-growing data collections; reduce data that wouldn't even fit in a database in the first place. We'll review in-production examples where each of these cases make unmodified Standard SQL, run and maintain over data streams in Kafka, and provide the functionality of bespoke stream processors.

Giga Spaces Data Grid / Data Caching Overview

jimliddle

Kafka in Context, Cloud, & Community (Simon Elliston Ball, Cloudera) Kafka Su...

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Kafka has fast become the center of streaming analytics applications in the modern digital enterprise. Kafka operates in the context of a broad ecosystem of data lifecycle components which need a consistent platform of security, monitoring, management and governance. This problem becomes paramount when your streaming architectures go hybrid by spanning from on-premises to the cloud. Throw in the reality of a multi-cloud setup that a lot of enterprises are facing and now, you have a complex streaming architecture that is difficult to operationally manage, monitor, secure or govern. Cloudera remains committed to an open community driven approach and increasing the ease of use and visibility for Kafka based solutions. Attend this session to understand more about how streaming architectures can be extended easily to the hybrid cloud and multi-cloud. Also, learn about our plans for further community contributions.

Today’s products - devices, software and services - are well instrumented to permit users, vendors and service providers to gather maximum insight into how they are used, when they need repair and many other operational insights. Ensuring that products can rapidly adapt to a constantly changing environment and changing customer needs requires that the events they generate are analyzed continuously and in context. Insights can be synthesized from many sources in context - geospatial and proximity, trajectory and even predicted future states.Customers, vendors and service providers need to analyze, learn, and predict directly from streaming events because data volumes are huge and automated responses must often be delivered in milliseconds. To achieve insights quickly, we need to build models on-the-fly whose predictions are accurate and in sync with the real world, often to support automation. Many insights depend on analyzing the joint evolution of data sources whose behavior is correlated in time or space.In this talk we present Swim, an Apache 2.0 licensed platform for continuous intelligence applications. Swim builds a fluid model of data sources and their changing relationships in real-time - Swim applications analyze, learn and predict directly from event data. Swim applications integrate with Apache Kafka for event streaming. Developers need nothing more than Java skills. Swim deploys native or in containers on k8s, with the same code in each instance. Instances link to build an application layer mesh that facilitates distribution and massive scale without sacrificing consistency. We will present several continuous intelligence applications in use today that depend on real-time analysis, learning and prediction to power automation and deliver responses that are in sync with the real-world. We will show how easy it is to build, deploy and run distributed, highly available event streaming applications that analyze data from hundreds of millions of sources - petabytes per day. The architecture is intuitively appealing and blazingly fast.

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As Kafka deployments grow within your organization, so do the challenges around lifecycle management. For instance, do you really know what streams exist, who is producing and consuming them? What is the effect of upstream changes? How is this information kept up to date, so it is relevant and consistent to others looking to reuse these streams? Ever wish you had a way to view and visualize graphically the relationships between schemas, topics and applications? In this talk we will show you how to do that and get more value from your Kafka Streaming infrastructure using an event portal. It’s like an API portal but specialized for event streams and publish/subscribe patterns. Join us to see how you can automatically discover event streams from your Kafka clusters, import them to a catalog and then leverage code gen capabilities to ease development of new applications.

Use ksqlDB to migrate core-banking processing from batch to streaming | Mark ...

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Core banking systems are batch oriented: typically with heavy overnight batch cycles before business opens each morning. In this talk I will explain some of the common interface points between core-banking infrastructure and event streaming systems. Then I will focus on how to do stream processing using ksqlDB for core-banking shaped data: showing how to do common operation using various ksqlDB functions. The key features are avro-record keys and multi-key joins (ksqlDB 0.15), schema management and state store planning.

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To remain competitive, organizations need to democratize access to fast analytics, not only to gain real-time insights on their business but also to power smart apps that need to react in the moment. In this session, you will learn how Kafka and SingleStore enable modern, yet simple data architecture to analyze both fast paced incoming data as well as large historical datasets. In particular, you will understand why SingleStore is well suited process data streams coming from Kafka.

Streaming data in the cloud with Confluent and MongoDB Atlas | Robert Waters,...

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Are you looking for a cloud-based architecture that includes the best of breed streaming and database technologies? In this session you will learn how to setup and configure the Confluent Cloud with MongoDB Atlas. We'll start the journey learning about the basic connectivity between the two cloud services and end with a brief discovery of what you can do with data once it is in MongoDB Atlas. By the end of this session you will know how to securely setup and configure the MongoDB Atlas connectors in the Confluent Cloud in both a source and sink configuration.

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While Azure provides native load balancing capabilities, our KEMP Virtual LoadMaster (VLM) significantly improves on these via advance features like application delivery and load balancing in Layer 7 of the network stack. Other features that KEMP VLM delivers for Azure based and hybrid infrastructure deployments are: - Client authentication and single sign-on (SSO) High Performance Layer 4 & Layer 7 Application Load Balancing - Intelligent Global Site Traffic Distribution - Application Health Checking - IP and Layer 7 Persistence - Content Switching - SSL Acceleration and Offload - Compression - Caching - Advanced App Gateway Services - Provide better Load Balancing over the Internal Load Balancer - Sophisticated Traffic Manager https://kemptechnologies.com/solutions/microsoft-load-balancing/loadmaster-azure/ https://azure.microsoft.com/en-us/marketplace/partners/kemptech/vlm-azure/

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Real-time connectivity of databases and systems is critical in enterprises adopting digital transformation to support super-fast decisioning to drive applications like fraud detection, digital payments, recommendation engines. This talk will focus on the many functions that database streaming serves with Kafka, Spark and Aerospike. We will explore how to eliminate the wall between transaction processing and analytics by synthesizing streaming data with system of record data, to gain key insights in real-time.

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Kubernetes Operators allow you to create custom resources in Kubernetes. They are popular for managing databases, which tend to be complex to manage. Our team built an operator to stand up ClickHouse, a popular open source data warehouse, in Kubernetes clusters. We'll share major learnings from this experience which we feel are applicable generally to running scalable, high performance databases in this environment. The talk starts with a level-set of Kubernetes, ClickHouse, and what an operator does. We'll then jump into the design of the ClickHouse operator example, covering challenges associated with the following problems:* Reducing the complexity of Kubernetes through definition of new resources for databases* Defining and managing storage* Performance, including comparative results which look pretty good* Monitoring* Upgrade and configuration changesKubernetes is not free from challenges, and we'll cover these as we touch on each point above. We'll conclude with a summary of reasons that we think Kubernetes is a great environment for data warehouses, based on our experience to date.

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Space-Based Architecture

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Data Integration with Apache Kafka: What, Why, How

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Presented at Orange County Advanced Analytics and Big Data Meetup, June 21 2019. Apache Kafka has fast become the dominant messaging technology for the enterprise; if you're a data scientist or data engineer and you have not yet worked with Kafka, that situation will likely change soon! In this session, Pat Patterson, director of evangelism at StreamSets, explains what Kafka is, why it has disrupted the previous generation of messaging products, and how you can use open source products to build dataflow pipelines with Kafka, without writing code.

Data Con LA 2019 - MetaConfig driven FeatureStore with Feature compute & Serv...

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MakeMyTrip - India's #1 online travel platform having more than 70% of the traffic from mobile apps embarked on a journey to revolutionize its customer experience by building a scalable, personalized, machine learning based platform which powers onboarding, in-funnel and post-funnel engagement flows, such as ranking, dynamic pricing, persuasions, cross-sell and propensity models.For a company like MakeMyTrip, the next wave of consumer growth is driven and powered by data products for personalization, context-aware mobile experiences. Having a better data architecture to ingest user activity streams (events), processing and data APIs enable a foundation for real-time feature generation for machine learning models.Topics include:* Why common feature-store, removing dataset fragmentation caused by usecase-by-usecase approach!* Productionizing ML via standardization : MetaConfigs & FeatureCatalog | Reducing Data-Tech Debt* Developing Real-Time Serving store over Spark Streaming, Kafka, RocksDB, Akka HTTP Data APIs* Lifecycle of feature generation | Online(Near Real-Time) & Historical(Batch) Compute* Consistent Feature Engineering & Model Deployment for DSA: DataScience AutomationAs Technology we leverage Kafka, Spark (Streaming, SQL), Scala, Python, AWS (S3, EMR, Glue and other services), DRUID, Hive, Presto, Cassandra, RocksDB, Redis, Akka HTTP

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OSDC 2019 | Democratizing Data at Go-JEK by Maulik Soneji

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Having grown 6666x in the last 3 years; the data generated has grown exponentially. As a Data Engineer at GoJEK, we faced the issue with having our complete team managing infrastructure requests. This led us to create an internal portal for other teams to self-provision their data. This talk is divided into two parts. In the first part, I will cover details about how we have scaled our data engineering infrastructure to manage the scale of more than 40 million messages per day. I will explain the data consumption, aggregation, monitoring and cold storage. This will also cover details about how we scaled our infrastructure to achieve the scale that we are at today. In the second part, I will cover how we created our internal portal for infrastructure orchestration. The infrastructure backed by kubernetes enables teams to self-provision data infrastructure without any supervision.

Whats New In GigaSpaces Xap 7.0

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This technical update shows the new and noteworthy in GigaSpaces XAP 7.0. It explains how the combination of a state-of-the-art in-memory data grid, a Jetty web container, and a grid-based business logic execution framework, forms a single, easy-to -use platform on which you can build and run extremely scalable applications, ranging from transactional systems through large-scale web applications to SaaS-delivered services. Key release highlights covered in this webinar include dramatic performance and scalability improvements, new monitoring and administration tools, and new data grid APIs.

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