DevOps Engineer (M-PESA) at Safaricom Ethiopia plc
Real User
Top 10
Aug 22, 2025
I'm working for a corporate that uses Apache HBase for their Big Data platform and I'm a Big Data engineer there. We're using a version of Apache HBase that is compatible with the other Big Data tools that we are using on the platform, but it's not the latest one. For Apache HBase, mostly we use it as a lookup database for queries that require doing lookups on the customer data or eligibility checks that we have to do for different kinds of customers. We store customer data on the Apache HBase database, and we do lookup jobs from those databases. I utilize the automatic sharding of Apache HBase. Sharding is a way of partitioning the data sets into readable segments to run the queries in the most optimized way. We use those sharding capabilities to optimize our queries and run them as fast as possible to utilize fewer resources because a Big Data platform uses many resources. To remove those necessities, we use sharding to partition and optimize our queries, which allows us to run our queries quickly without consuming as much CPU and memory resources. Apache HBase processing works by using in-memory data resources and takes advantage of the in-memory utilities without relying on storage capabilities. The documentation I used is generally good, but the visualization could improve; it seems outdated. However, since it's an open-source tool, one cannot expect everything to be perfect, and the maintainers are typically driven by passion rather than finances. Overall, it's good documentation, and I've referenced it to address various problems and implementations. Based on my experience, I would rate Apache HBase an eight out of ten. I wonder if there are any other options that you would recommend?
I would not recommend Apache HBase to other users. There are more efficient solutions available in the market that have fixed many limitations presented by Apache HBase. Overall, I rate Apache HBase a four out of ten.
NoSQL Databases are designed to store and process large volumes of unstructured data quickly. They are often used in real-time web applications and big data environments where speed and flexibility are crucial. These databases do not rely on a fixed schema, providing flexibility and scalability to handle diverse data types. They are highly distributed and can manage large data volumes across many servers, making them an attractive choice for businesses dealing with high transaction rates...
I'm working for a corporate that uses Apache HBase for their Big Data platform and I'm a Big Data engineer there. We're using a version of Apache HBase that is compatible with the other Big Data tools that we are using on the platform, but it's not the latest one. For Apache HBase, mostly we use it as a lookup database for queries that require doing lookups on the customer data or eligibility checks that we have to do for different kinds of customers. We store customer data on the Apache HBase database, and we do lookup jobs from those databases. I utilize the automatic sharding of Apache HBase. Sharding is a way of partitioning the data sets into readable segments to run the queries in the most optimized way. We use those sharding capabilities to optimize our queries and run them as fast as possible to utilize fewer resources because a Big Data platform uses many resources. To remove those necessities, we use sharding to partition and optimize our queries, which allows us to run our queries quickly without consuming as much CPU and memory resources. Apache HBase processing works by using in-memory data resources and takes advantage of the in-memory utilities without relying on storage capabilities. The documentation I used is generally good, but the visualization could improve; it seems outdated. However, since it's an open-source tool, one cannot expect everything to be perfect, and the maintainers are typically driven by passion rather than finances. Overall, it's good documentation, and I've referenced it to address various problems and implementations. Based on my experience, I would rate Apache HBase an eight out of ten. I wonder if there are any other options that you would recommend?
It's better to use AWS DynamoDB or Cassandra. I would rate it an eight out of ten. It is easy for a beginner to learn.
I would not recommend Apache HBase to other users. There are more efficient solutions available in the market that have fixed many limitations presented by Apache HBase. Overall, I rate Apache HBase a four out of ten.