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Object databases Object databases store data not in terms of relations and columns and rows, but in terms of objects as understood from the discipline of object-oriented programming. This makes it straightforward to use these databases from object-oriented applications. Object databases such as db4o and InterSystems Cache allow you to avoid techniques like stored procedures and object-relational mapping (ORM) tools. The most widely used object database is Amazon Web Services’ Simple Storage Service (S3). XML databases XML databases are a … Continue reading “Categories of NoSQL Databases-II”

Key-value stores In a key-value store, the data items are keys that have a set of attributes. All data relevant to a key is stored with the key; data is frequently duplicated. Popular key-value stores include Amazon’s Dynamo DB, Riak, and Voldemort. Additionally, many popular caching technologies act as key-value stores, including Oracle Coherence, Redis, and Memcached. Column stores In a column store, also known as a wide-column store or column-oriented store, data is stored by column rather than by … Continue reading “Categories of NoSQL Databases-I”

Metrics and loggingKafka is also ideal for collecting application and system metrics and logs. This is a use case in which the ability to have multiple applications producing the same type of message shines. Applications publish metrics on a regular basis to a Kafka topic, and those metrics can be consumed by systems for monitoring and alerting. They can also be used in an offline system like Hadoop to perform longer-term analysis, such as growth projections. Log messages can be … Continue reading “Kafka Use Cases-II”

Activity tracking The original use case for Kafka, as it was designed at LinkedIn, is that of user activity tracking. A website’s users interact with frontend applications, which generate messages regarding actions the user is taking. This can be passive information, such as page views and click tracking, or it can be more complex actions, such as information that a user adds to their profile. The messages are published to one or more topics, which are then consumed by applications … Continue reading “Kafka Use Cases-I”

Scalable Kafka’s flexible scalability makes it easy to handle any amount of data. Users can start with a single broker as a proof of concept, expand to a small development cluster of three brokers, and move into production with a larger cluster of tens or even hundreds of brokers that grows over time as the data scales up. Expansions can be performed while the cluster is online, with no impact on the availability of the system as a whole. This … Continue reading “Why Kafka? II”

Why Kafka?-I Multiple Producers Kafka is able to seamlessly handle multiple producers, whether those clients are using many topics or the same topic. This makes the system ideal for aggregating data from many frontend systems and making it consistent. For example, a site that serves content to users via a number of microservices can have a single topic for page views that all services can write to using a common format. Consumer applications can then receive a single stream of … Continue reading “Why Kafka?- I”