What type of data makes up the rows? Relational databases fit many data persistance problems, however there are other types of databases which might be more applicable to your problem. Essentially, use the right tool for the job.
Relational, key/value, document, columnar, graph. Within each of those types there are specific implmentations (mysql, postgres, oracle, redis, riak, neo4j, titan, cassandra, mongodb, couchdb, etc), each with their own pros/cons.
Within the relational database realm, make sure you understand: when and when not to normalize/denormalize, clustered/non-clustered indexes, btree/rtree/hash indexes, schema design, storage engines, partitioning, master/slave, replication, caching strategies, execution plan optimization, etc..
Good point, our biggest table consists mostly of numeric data, with the exception of a MAC type of address and date/time columns. I'm going to do more research on your last point as well. Thank you!
Comments
What type of data makes up the rows? Relational databases fit many data persistance problems, however there are other types of databases which might be more applicable to your problem. Essentially, use the right tool for the job.
Relational, key/value, document, columnar, graph. Within each of those types there are specific implmentations (mysql, postgres, oracle, redis, riak, neo4j, titan, cassandra, mongodb, couchdb, etc), each with their own pros/cons.
Within the relational database realm, make sure you understand: when and when not to normalize/denormalize, clustered/non-clustered indexes, btree/rtree/hash indexes, schema design, storage engines, partitioning, master/slave, replication, caching strategies, execution plan optimization, etc..
Good point, our biggest table consists mostly of numeric data, with the exception of a MAC type of address and date/time columns. I'm going to do more research on your last point as well. Thank you!