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for the complete documentation index see llms txt https docs pinot apache org llms txt markdown versions of documentation pages are available by appending md to page urls this page is available as markdown https docs pinot apache org readme md introduction apache pinot is a real time distributed online analytical processing olap datastore use pinot to ingest and immediately query data from streaming or batch data sources including apache kafka amazon kinesis hadoop hdfs amazon s3 azure adls and google cloud storage hint style info we d love to hear from you join us in our slack channel https inviter co apache pinot to ask questions troubleshoot and share feedback endhint apache pinot includes the following ultra low latency analytics even at extremely high throughput columnar data store with several smart indexing and pre aggregation techniques scaling up and out with no upper bound consistent performance based on the size of your cluster and an expected query per second qps threshold it s perfect for user facing real time analytics and other analytical use cases including internal dashboards anomaly detection and ad hoc data exploration embed url what is apache pinot and user facing analytics by tim berglund endembed user facing real time analytics user facing analytics refers to the analytical tools exposed to the end users of your product in a user facing analytics application all users receive personalized analytics on their devices resulting in hundreds of thousands of queries per second queries triggered by apps may grow quickly in proportion to the number of active users on the app as many as millions of events per second data generated in pinot is immediately available for analytics in latencies under one second user facing real time analytics requires the following fresh data the system needs to be able to ingest data in real time and make it available for querying also in real time support for high velocity highly dimensional event data from a wide range of actions and from multiple sources low latency queries are triggered by end users interacting with apps resulting in hundreds of thousands of queries per second with arbitrary patterns reliability and high availability scalability low cost to serve why pinot pinot is designed to execute olap queries with low latency it works well where you need fast analytics such as aggregations on both mutable and immutable data user facing real time analytics pinot was originally built at linkedin to power rich interactive real time analytics applications such as who viewed profile https www linkedin com me profile views urn li wvmp summary company analytics https www linkedin com company linkedin insights talent insights https business linkedin com talent solutions talent insights and many more ubereats restaurant manager https eng uber com restaurant manager is another example of a user facing analytics app built with pinot real time dashboards for business metrics pinot can perform typical analytical operations such as slice and dice drill down roll up and pivot on large scale multi dimensional data for instance at linkedin pinot powers dashboards for thousands of business metrics connect various business intelligence bi tools such as superset https superset apache org docs intro tableau https www tableau com resource business intelligence or powerbi https powerbi microsoft com en us to visualize data in pinot enterprise business intelligence for analysts and data scientists pinot works well as a highly scalable data platform for business intelligence pinot converges big data platforms with the traditional role of a data warehouse making it a suitable replacement for analysis and reporting enterprise application development for application developers pinot works well as an aggregate store that sources events from streaming data sources such as kafka and makes it available for a query using sql you can also use pinot to aggregate data across a microservice architecture into one easily queryable view of the domain pinot tenants architecture and concepts components cluster tenant md prevent any possibility of sharing ownership of database tables across microservice teams developers can create their own query models of data from multiple systems of record depending on their use case and needs as with all aggregate stores query models are eventually consistent get started if you re new to pinot take a look at our getting started guide content ref url pages lth74ib7k9iuocsgdh4 start here start here getting started md endcontent ref to start importing data into pinot see how to import batch and stream data content ref url pages yqykvolqrbderhikxqke ingestion build with pinot ingestion md endcontent ref to start querying data in pinot check out our query guide content ref url pages yljexjhighcd3nirorft querying sql build with pinot querying and sql md endcontent ref learn for a conceptual overview that explains how pinot works check out the concepts guide content ref url pages m1smdqwpb80r _um6nmz concepts architecture and concepts concepts md endcontent ref to understand the distributed systems architecture that explains pinot s operating model take a look at our basic architecture section content ref url pages m1smg1cdvufjsjd1td8 architecture architecture and concepts concepts architecture md endcontent ref agent instructions this documentation is published with gitbook gitbook is the documentation platform designed so that both humans and ai agents can read navigate and reason over technical content effectively learn more at gitbook com querying this documentation if you need additional information that is not directly available in this page you can query the documentation dynamically by asking a question perform an http get request on the current page url with the ask query parameter and the optional goal query parameter get https docs pinot apache org readme md ask goal ask is the immediate question it should be specific self contained and written in natural language goal is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user gitbook uses it to tailor the answer towards what is most useful for that goal the response will contain a direct answer to the question and relevant excerpts and sources from the documentation use this mechanism when the answer is not explicitly present in the current page you need clarification or additional context or you want to retrieve related documentation sections