Apache Hive vs. Redis Software

Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.

Incentivized

Redis has been a great investment for our organization as we needed a solution for high speed data caching. The ramp up and integration was quite easy. Redis handles automatic failover internally, so no crashes provides high availability. On the fly scaling scale to more/less cores and memory as and when needed.

Incentivized

• Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
• Relatively easy to set up and start using.
• Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.

Incentivized

• Easy for developers to understand. Unlike Riak, which I've used in the past, it's fast without having to worry about eventual consistency.
• Reliable. With a proper multi-node configuration, it can handle failover instantly.
• Configurable. We primarily still use Memcache for caching but one of the teams uses Redis for both long-term storage and temporary expiry keys without taking on another external dependency.
• Fast. We process tens of thousands of RPS and it doesn't skip a beat.

Incentivized

• Some queries, particularly complex joins, are still quite slow and can take hours
• Previous jobs and queries are not stored sometimes
• Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
• Sometimes, directories and tables don't load properly which causes confusion

Incentivized

• We had some difficulty scaling Redis without it becoming prohibitively expensive.
• Redis has very simple search capabilities, which means its not suitable for all use cases.
• Redis doesn't have good native support for storing data in object form and many libraries built over it return data as a string, meaning you need build your own serialization layer over it.

Incentivized

Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.

We will definitely continue using Redis because: 1. It is free and open source. 2. We already use it in so many applications, it will be hard for us to let go. 3. There isn't another competitive product that we know of that gives a better performance. 4. We never had any major issues with Redis, so no point turning our backs.

Incentivized

Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.

Incentivized

It is quite simple to set up for the purpose of managing user sessions in the backend. It can be easily integrated with other products or technologies, such as Spring in Java. If you need to actually display the data stored in Redis in your application this is a bit difficult to understand initially but is possible.

Incentivized

Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.

Incentivized

The support team has always been excellent in handling our mostly questions, rarely problems. They are responsive, find the solution and get us moving forward again. I have never had to escalate a case with them. They have always solved our problems in a very timely manner. I highly commend the support team.

Incentivized

Whitelisting of the AWS lambda functions.

Incentivized

Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed

Incentivized

We are big users of MySQL and PostgreSQL. We were looking at replacing our aging web page caching technology and found that we could do it in SQL, but there was a NoSQL movement happening at the time. We dabbled a bit in the NoSQL scene just to get an idea of what it was about and whether it was for us. We tried a bunch, but I can only seem to remember Mongo and Couch. Mongo had big issues early on that drove us to Redis and we couldn't quite figure out how to deploy couch.

Incentivized

• Apache hive is secured and scalable solution that helps in increasing the overall organization productivity.
• Apache hive can handle and process large amount of data in a sufficient time manner.
• It simplifies writing SQL queries, hence helping the organization as most companies use SQL for all query jobs.

Incentivized

• Redis has helped us increase our throughput and server data to a growing amount of traffic while keeping our app fast. We couldn't have grown without the ability to easily cache data that Redis provides.
• Redis has helped us decrease the load on our database. By being able to scale up and cache important data, we reduce the load on our database reducing costs and infra issues.
• Running a Redis node on something like AWS can be costly, but it is often a requirement for scaling a company. If you need data quickly and your business is already a positive ROI, Redis is worth the investment.

Incentivized