Happy new year to you all!
Last time we discussed what is Big data and how it relates to No SQL. This time I am going to talk about two different kind of data storage and retrieval requirements: Operational and Analytical.
Operational and analytical workloads for Big Data present opposing requirements and systems have evolved to address their particular demands separately and in very different ways. Operational workloads are supposed to be highly concurrent, low latency, very selective. While analytical workloads are query intensive looking at a huge data set.
No SQL DBs mainly help on operational side of the spectrum.
Operational vs Analytical overview
Last time we discussed what is Big data and how it relates to No SQL. This time I am going to talk about two different kind of data storage and retrieval requirements: Operational and Analytical.
Operational and analytical workloads for Big Data present opposing requirements and systems have evolved to address their particular demands separately and in very different ways. Operational workloads are supposed to be highly concurrent, low latency, very selective. While analytical workloads are query intensive looking at a huge data set.
No SQL DBs mainly help on operational side of the spectrum.
Operational vs Analytical overview
| OPERATIONAL | ANALYTICAL | |
| Latency | 1 ms - 100 ms | 1
min - 100 min |
| Concurrency | 1000 - 100,000 | 1
to 10 |
| Access Pattern | Writes and Reads | Reads |
| Queries | Selective | Unselective |
| Data Scope | Operational | Retrospective |
| End User | Customer | Data
Scientist |
| Technology | NoSQL | MapReduce, MPP Database |
Next post I will try to conclude the SQL- NO SQL topic
No comments:
Post a Comment