Kyvos Key Concepts
BI acceleration layer
Kyvos builds a semantic layer on the cloud and on-premises data lakes, bridging the gap between your data and the business intelligence and analytics tools. This layer provides a consistent semantic model for business users, making it easy for them to visualize massive data. It allows them to see the dimensions and measures available to them and drag and drop them into their visualizations intuitively using the BI tool of their choice.
The next layer of the Kyvos architecture consists of the BI server cluster, which enables the aggregation of data at a massive scale. The BI servers are designed with active-active load balancing for scalable deployments to thousands of users across the enterprise. The built-in load balancer intelligently routes queries to the correct server, maximizing performance and capacity utilization. Multiple BI servers ensure high availability with minimal downtime. In the case of a BI server failure, another one is automatically promoted for use.
When the user fires a query, the BI tools connect to the Kyvos BI server using standard connectors. The BI server parses the query and routes it to the query engines, which execute it and return the results to the BI tool. The Kyvos query engine cluster is highly elastic. The number of active query engines can be easily increased or decreased to deal with varying loads, accommodate more users, or further reduce response times. Kyvos query engines are optimized to return most queries in less than a second.
Besides this, Kyvos has an intelligent, multi-level caching mechanism that ensures high performance based on query patterns and usage. It stores segments of the most frequently queried semantic models and caches the results of the most frequent queries in high-performing storage.
Query Engines
Query Engines (QEs) are the compute layer in Kyvos’ architecture that enable fast, scalable, and interactive analytics on massive data volumes. They are distributed computing components responsible for processing analytical queries on data semantic models and datasets, playing a critical role in delivering high-performance, interactive analytics at scale. QEs receive SQL or MDX queries—often generated from BI tools or Kyvos Dialogs—and execute them efficiently against Kyvos’ multidimensional data semantic models or underlying datasets. Multiple QEs can run in parallel, allowing Kyvos to distribute query workloads, handle large numbers of concurrent users, and process complex analytical queries. Additionally, QEs integrate seamlessly with various BI tools, APIs, and custom applications to deliver analytical results quickly and reliably.
Kyvos Web Portal
The Kyvos Web Portal provides a centralized, user-friendly environment for both business users and technical teams to perform self-service analytics, manage large-scale data environments, and derive insights efficiently.
Kyvos Manager
Kyvos Manager is the administrative and orchestration tool provided by Kyvos to manage the deployment, configuration, monitoring, and maintenance of Kyvos clusters and services. It offers a centralized interface that simplifies the operational management of the Kyvos platform across cloud or on-premises environments. For more details, see Kyvos Manager Management and Monitoring Guide
Caching
Caching in Kyvos refers to the mechanism of storing computed query results and aggregated data to accelerate analytical queries and deliver faster performance. Instead of querying the raw data every time, Kyvos retrieves results directly from its caches, significantly reducing response times and computational overhead. For more details, see the Setting up and using cache rules section.
Kyvos primarily uses two types of caching:
Aggregate Cache (Smart Aggregate Cache): Stores aggregated values calculated during aggregate builds, enabling Kyvos to serve complex analytical queries instantly without scanning large volumes of raw data.
Query Result Cache: Retains results of previously executed queries, allowing identical or similar queries to be answered instantly from cache rather than recalculating results.
Scaling
Kyvos supports scaling QEs up or down dynamically to meet workload demands, optimizing both performance and resource utilization. For more details, see the Managing system scheduling and capacity section.
Data platforms
Kyvos offers native support for Amazon Web Services (AWS), Google Cloud, Microsoft Azure, Cloudera, Hortonworks, MapR, and Apache Hadoop. It also supports all cloud data warehouses, such as Snowflake, Amazon Redshift, and Google BigQuery, and the latest releases of Cloudera, MapR, and Apache Hadoop.
As Kyvos processes and serves queries directly on your data platform, there is no need for any data movement. This saves you from the trouble of sending high-volume data over the network and ensures that your data is safe. Further, the Kyvos semantic models are stored on the data platform, so there are no additional infrastructure requirements. The Kyvos BI server and query engines can be deployed directly on your existing cloud or on-premise data platform.
Business intelligence and analytics tools
The topmost layer of the Kyvos architecture consists of BI and analytics tools that connect to Kyvos semantic models using standard access mechanisms such as SQL and MDX, enabling users to access massive data instantly and interactively using their choice of BI tools. Kyvos supports all major BI tools, including Business Objects, Cognos, Excel, Strategy, Power BI, Qlik, Spotfire, and Tableau. Kyvos semantic models can also be accessed from data science engines like R and Python to discover data patterns. It supports REST and JAVA APIs that enable integration with custom enterprise applications.
Kyvos also has a native visualization engine with an intuitive drag-and-drop interface for self-service analysis.
Elasticity
Kyvos is architected to optimize resource utilization, deal with peak loads, and deliver cost-effective BI on the cloud. The solution can quickly scale up and down without disruption to deliver consistent performance and ensure optimal utilization of resources. As more data gets added to the BI environment, Kyvos scales out transparently to process semantic models on that data. Similarly, querying capacity can be scaled up or down depending on the expected load. This feature also allows it to support thousands of concurrent users without any impact on performance.
Security
Kyvos offers high-class, enterprise-level security through its built-in security model and support for standard security frameworks and protocols. It enables granular access control with column and row-level security processed into the system. Additionally, it integrates with enterprise security systems like Knox, Ranger, and Sentry, as well as custom security frameworks. Kyvos supports Kerberos authentication and LDAP for enterprise user management and integrates with enterprise single sign-on tools such as SiteMinder and Okta.