Handling content: In today's world, a variety of content is available for the users for consumption. Also, the variety of application clients, such as browser, mobile, and so on, leads to an additional requirement where each client needs the same content in different formats. Users not only consume content but also generate a variety of content in a large volume with a high velocity, such as tweets, Facebook posts, images, bloging, and many more. HBase is the perfect choice as the backend of such applications, for example, many scalable content management solutions are using HBase as their backend.

Handling incremental data: In many use cases, trickled data is added to a data store for further usage, such as analytics, processing, and serving. This trickled data could be coming from an advertisement's impressions such as clickstreams and user interaction data or it can be time series data. HBase is used for storage in all such cases. For example, Open Time Series Database (OpenTSDB) uses HBase for data storage and metrics generation. The counters feature (discussed in Chapter 5, The HBase Advanced API) is used by Facebook for counting and storing the "likes" for a particular page/image/post.

Facebook (www.facebook.com): Facebook is using HBase to power its message infrastructure. Facebook opted for HBase to scale from their old messages infrastructure which handled over 350 million users, sending over 15 billion person-to-person messages per month. HBase was selected due to the excellent scalability and performance for big workloads, along with autoload balancing and failover features and so on. Facebook also uses HBase for counting and storing the "likes" contributed by users.

Meetup (www.meetup.com): Meetup uses HBase to power a site-wide, real-time activity feed system for all of its members and groups. In its architecture, group activity is written directly to HBase and indexed per member, with the member's custom feed served directly from HBase for incoming requests.

Twitter (www.twitter.com): Twitter uses HBase to provide a distributed, read/write backup of all the transactional tables in Twitter's production backend. Later, this backup is used to run MapReduce jobs over the data. Additionally, its operations team uses HBase as a time series database for cluster-wide monitoring / performance data.

Yahoo (www.yahoo.com): Yahoo uses HBase to store document fingerprints for detecting near-duplications. With millions of rows in the HBase table, Yahoo runs a query for finding duplicated documents with real-time traffic.