Here are a few of the other characteristics of a modern data hub. When you hear “customer 360,” or a 360-degree view of some … In fact, in most use cases, a modern hub collects and merges data on the fly, then passes the newly instantiated data set to a target user, app, or database with zero persistence or temporary persistence (for staging) at the hub. Apache Kafka (https://kafka.apache.org/ is a distributed streaming platform for publishing and subscribing records as well as storing and processing streams of records. This includes multiple forms of metadata (technical, business, and operational metadata) as well as search indices, domain glossaries, and browseable data catalogs. Security and Governance include tools for providing services, data and API security and governance. Currently, we have investigated Hive Metastore as a solution that provides an SQL interface to access the metadata information. AI Library provides REST interface access to pre-trained and validated served models for several AI based services including sentiment analysis, flake analysis and duplicate bug detection. A modern data hub is the opposite: there is little or no persistence at the hub, and in most use cases data collected by the hub is immediately shared with many users and applications. A complete end-to-end AI platform requires services for each step of the AI workflow. Figure 2 displays a high level architecture diagram of ODH as an end-to-end AI platform running on OpenShift Container platform. Generally this data distribution is in the form of a hub and spoke architecture. It is a collection of open source tools and services natively running on OpenShift. The modern data hub differs sharply from old-fashioned ones. Think of the data views, semantic layers, orchestration, and data pipelines just discussed. Some of the components within the ODH platform are also operators such as Apache Spark™. You can reach him at prussom@tdwi.org, @prussom on Twitter, and on LinkedIn at linkedin.com/in/philiprussom. https://tdwi.org/articles/2019/09/09/arch-all-benefits-of-a-modern-data-hub.aspx TDWI Members have access to exclusive research reports, publications, communities and training. Seldon is a tool that provides model hosting and metric collection from both the model itself and the component serving the model. Open Data Hub is a blueprint for building an AI as a service platform on Red Hat's Kubernetes-based OpenShift® Container Platform and Ceph Object Storage. Data scientists can use familiar tools such as Jupyter notebooks for developing complex algorithms and models. Once the models are trained and validated accordingly, they are ready to be served on the production platform in the last phase of the AI end-to-end workflow. Apache Spark™is installed as an operator on OCP providing cluster wide custom resource to launch distributed AI workloads on distributed spark clusters. Currently ,when installing the ODH operator it includes the following components: Ceph, Apache Spark, Jupyterhub, Prometheus and Grafana. For data storage and availability, ODH provides Ceph, with multi protocol support including block, file and S3 object API support, both for persistent storage within the containers and as a scalable object storage data lake that AI applications can store and access data from . In general, an AI workflow includes most of the steps shown in Figure 1 and is used by multiple AI engineering personas such as Data Engineers, Data Scientists and DevOps. Red Hat® OpenShift® Container Platform is the leading Kubernetes based container platform providing multiple functionalities for successfully running distributed AI workloads. Metrics can be custom model metrics or Seldon core system metrics. For the Data Scientist development environment, ODH provides Jupyter Hub and Jupyter Notebook images running natively distributed on OpenShift. The Data Integration Hub. Applications send tasks to executors using the SparkContext and these executors run the tasks on the cluster nodes they are assigned to. This allows for resource management isolation. High performance in-memory datastore solutions such as Red Hat Data Grid which is based on Infinispan are essential for fast data access needed for analysis or model training. Storage: Data Lake/Databases/In-Memory includes tools for distributed file, block and object storage at scale. This way, unique views -- for diverse business functions, from marketing to analytics to customer service -- can be created in a quick and agile fashion without migration projects that are time-consuming and disruptive for business processes and users. Data sources. Privacy Policy As just discussed, the hub does not consolidate silos as a way of centralizing and standardizing data. Operators manage custom resources that provide specific cluster wide functionalities. Data Hub enables you to run your existing Cloudera platform in the cloud through lift-and-shift with improved performance, robust governance, and availability as experienced by thousands of … The following diagram shows the logical components that fit into a big data architecture. A subset of these components and tools are included in the ODH release available today and the rest are scheduled to be integrated in future releases as described in the roadmap section below. For data storage and availability, ODH provides Ceph , with multi protocol support including block, file and S3 object API support, both for persistent storage within the containers and as a scalable object storage data lake that AI applications can store and access data … The hub's integrated tooling makes this happen through a massive library of interfaces and deep support for new technologies, data types, and platforms. 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