Purpose-Built For Mission-Critical Use Cases

Power geo-distributed apps needing zero data loss and multi-datacenter availability

User Identity
User Identity

Description

Enables users to login into accounts by verifying their identity (commonly username and password) as well as to view/edit their online profile. This functionality is a critical part of most modern web or mobile applications.

Requirements

  • The users interacting with these applications are often geographically distributed making it essential to replicate and serve data from mutiple regions.

  • Data integrity and strong consistency are extremely important, since user identity verification is essential to enforcing privacy and security of user data.

  • Automatic failure handling for node/rack/AZ/DC failures is desirable. A failure would impact the ability of the user to interact with the application.

  • Low latency read requests are important to ensure a good user experience irrespective of their geographic location.

  • Ability to handle compliance requirements, where some countries require companies to keep the data of their citizens in datacenters present inside those countries.

User Identity

Why YugaByte?

Flexible geo-partitioned, multi-datacenter deployments - It is very easy to configure YugaByte to replicate data using sync or async replication across multiple datacenters. YugaByte has a very high write availability even in a cross-datacenters configuration (as long as majority of replicas are available). It also supports one-click deployment of async replicas in far away regions. This also makes it easy to satisfy compliance requirements.

Strong write consistency - YugaByte is strongly consistent database offering single row ACID transactions as the default. This ensures data correctness even in failure scenarios.

Tunable read consistency - YugaByte can be easily configured to perform low-latency reads from local or nearby datacenters. This allows the read latencies to be low across all regions without sacrificing its strong consistency semantics.

Similar Applications

Online marketplace, shopping cart, product catalog

Financial Data Service
Financial Data Service

Description

Applications providing financial data services such as realtime stock quotes, finance portfolio management and related news. These applications offer a good degree of customization for the end user, for example allowing users to subscribe to realtime updates for certain stock symbols.

Requirements

  • The application needs to evolve rapidly based on usage patterns, and therefore the database layer should be able to scale out easily.

  • One challenge with current solutions is that the entire data needs to fit in memory in the cache tier.

  • Reads for recent data should have predictably low latencies. For instance, reads for older data should not cause reads for recent data to have high latencies.

  • The underlying database should support low-latency reads and writes to ensure a good user experience.

Financial Data Service

Why YugaByte?

Reliable, fast scale-out - These applications may evolve rapidly or may get a sudden increase in the number of queries due to a higher than expected adoption by end users (this is a good thing). With YugaByte, the database layer can be scaled out reliably and efficiently without any impact to the currently running application.

Scan resistant cache - YugaByte has an internal scan resistant cache while ensures that larger reads do no pollute the cache. This means that the common read pattern such as reads for recent data are not impacted by a larger read.

Integrated cache and DB - With YugaByte, the entire data set does not need to fit in memory (unlike with a traditional cache like Redis often used to serve reads in such applications). Older or inactive data is automatically paged out of RAM to persistent storage. This allows for a larger data to be served with fewer machines.

Tiering of data - A lot of these applications have fast growing data, and therefore would need to keep storage costs under control. YugaByte offers both the ability to automatically tier colder data to cheaper storage layer as well as the ability to efficiently expire older data.

Similar Applications

Order history, audit trail

IoT database platform
IoT database platform

Description

An application that keeps track of sensors deployed at various geo-locations as well as the metric data emitted by them. The data from the sensors needs to be processed, stored and quickly analyzed.

Requirements

The typical IoT application architecture consists of a variable set of devices/sensors that collect data. This data is transported and stored in a backend datacenter using a distributed messaging system. The data flows from the messaging store in the backend datacenter into a stateless processing tier, which transforming the data according to the business logic. The transformed data is written to a database for serving to the end users or for consumption by other services using APIs.

  • The number of deployed devices and the ingest rate can vary rapidly, requiring the application and the database to scale out (or shrink) reliably and efficiently.

  • The application has a high write rate and ever growing data sets due to the always-on nature of these connected devices.

  • Data that is collected is often analyzed and the result of the analysis is presented to end users or other systems using APIs. The database should be integrated with good frameworks for data analysis like Apache Spark.

  • Data comes from different geographic locations, and may need to be processed in different datacenters.

IoT database platform

Why YugaByte?

Reliable, fast scale-out - With YugaByte, the database layer can be scaled out reliably and efficiently without any impact to the currently running application. This makes it very easy to constantly resize the database and change machine types without any interruption to the application.

Tiering of data - A lot of IoT applications have fast growing data. YugaByte handles large data sets efficiently. Additionally, YugaByte offers both the ability to automatically tier colder data to cheaper storage layer as well as the ability to efficiently expire older data.

Integrated with Apache Spark and SMACK stack YugaByte works well for real-time analytics with Apache Spark. It stores data in a time-partitioned manner making it very efficient at retrieving recent data.

Flexible geo-partitioned, multi-datacenter deployments - It is very easy to run and manage YugaByte clusters across multiple datacenters, cloud providers and gregraphic regions. It also supports one-click deployment of async replicas in far away regions.

Similar Applications

Timeseries applications, metrics based monitoring

User Personalization
User Personalization

Description

Build an application that can tailor a service, content or a product in an on-going manner such that it is more relevant to specific individual users as well as groups or segments of users. The aim is to improve customer satisfaction, digital sales conversion, branding and other such key metrics.

Requirements

  • Needs to serve end-user facing queries with low latencies in the foreground, and analyze data in the background constantly.

  • Data for user personalization flows in from multiple sources. Examples of these sources include the user’s profile, their interaction history (order history) with the business and their search patterns on the website. The data layer should be able to handle a high write throughput from all these sources.

  • The collected data needs to be analyzed in order to come up with good recommendations for personalization for that user, which are written back into the serving tier to show the user. Apache Spark is a commonly used framework to perform this analysis.

  • The analysis needs to happen in a nearly realtime fashion, therefore using a traditional data warehouse which takes multiple hours for analysing the data is not ideal.

User Personalization

Why YugaByte?

Strong consistency with high performance - YugaByte offers strong consistency by default, without giving up on performance. It can perform low latency reads and writes.

Unified serving and analytics tier - Simplify the data layer by unifying the serving tier which serves the data to the end user and the analytics tier which computes the recommendations for the users. Eliminate the need for an ETL pipeline and a data warehouse. This makes it easy to deploy, manage and gives near realtime results.

Reliable, fast scale-out - With YugaByte, the database layer can be scaled out reliably and efficiently without any impact to the currently running application. This makes it very easy to constantly resize the database for the serving tier as well as the analytics tier. YugaByte also makes it operationally easy to change machine types without any interruption to the application.

Integrated with Apache Spark and SMACK stack - YugaByte works well for real-time analytics with Apache Spark. It stores data in a time-partitioned manner making it very efficient at retrieving recent data.

Similar Applications

360-degree customer view, fraud detection

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