HBase vs SQLite: What are the differences?
Developers describe HBase as "The Hadoop database, a distributed, scalable, big data store". Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. On the other hand, SQLite is detailed as "A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine". SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.
HBase and SQLite can be primarily classified as "Databases" tools.
"Performance" is the top reason why over 7 developers like HBase, while over 151 developers mention "Lightweight" as the leading cause for choosing SQLite.
HBase is an open source tool with 2.91K GitHub stars and 2.01K GitHub forks. Here's a link to HBase's open source repository on GitHub.
Intuit, Coderus, and Infoshare are some of the popular companies that use SQLite, whereas HBase is used by Pinterest, HubSpot, and Yammer. SQLite has a broader approval, being mentioned in 314 company stacks & 477 developers stacks; compared to HBase, which is listed in 54 company stacks and 18 developer stacks.
What is HBase?
What is SQLite?
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SQLite is a tricky beast. It's great if you're working single-threaded, but a Terrible Idea if you've got more than one concurrent connection. You use it because it's easy to setup, light, and portable (it's just a file).
In Paperless, we've built a self-hosted web application, so it makes sense to standardise on something small & light, and as we don't have to worry about multiple connections (it's just you using the app), it's a perfect fit.
For users wanting to scale Paperless up to a multi-user environment though, we do provide the hooks to switch to PostgreSQL .
The final output is inserted into HBase to serve the experiment dashboard. We also load the output data to Redshift for ad-hoc analysis. For real-time experiment data processing, we use Storm to tail Kafka and process data in real-time and insert metrics into MySQL, so we could identify group allocation problems and send out real-time alerts and metrics.
Used during the "build process" of Coolfront Mobile's Flat rate search engine database. Flat rate data that resides in Salesforce is transformed using SQLite into a format that is usable for our mobile Flat rate search engine (AKA: Charlie).
RDBTools is a self-hosted application, and it is important that the installation process is simple. With SQLite, we create a new database file for every analysis. Once the analysis is done, the SQLite file can be thrown away easily.
All the dynamic data (i.e.: jobs) is stored in a simple SQLite database.
Все динамические данные (вакансии) хранятся в простой SQLite БД.
There's really no call for something heavier for this site. SQLite is simple, easy to use and quite reliable given its age.