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An Ultimate Guide To Non-Traditional Data Storage For Engineers

It is already the era of big data, and that comes with great demands for data experts and engineers. Decades ago, these people only had to deal with simple roles of developing data extraction and analysis channels. Indeed this did not require rocket science. Fast forward to today, the big data engineers have to create complicated and challenging channels to cope with the great demands of the big data. The main concern of all stakeholders on this concept is storage and retrieval of data as well as analysis and use. Traditionally, people have known only the SQL databases which don’t get along well with complex and huge data. Thus, today the experts have come up with new ideas to handle the demand.

Search engines: elastic search

Most people with basic computer knowledge may not know how search engines work like a database and their capabilities. However, an input of keywords will work similar to using a query in MySQL, but the search engine will provide many capabilities. Elastic search which may seem like a simple approach is, fortunately, a detailed text search through layered aggregation. This concept has been used for a while by data engineers and still proves to be powerful in its functions.

How search engines work as a data store

  • They break down fields and create many index keys using the analyzer module
  • The retrieval of information is ranked depending on the number of keywords that match
  • The data is routed using shards or Lucene instances using individual fields.
  • Aggregating is done using bucketing method

Document stores: Mongo DB

The Mongo DB is used as an example since it provides the best schema adaptations approach when storing documents. In fact, this kind of storage does not need prior defining of schema approach. This method is one of the NoSQL approach to data handling with its ease of use gaining recognition worldwide. People can handle any form of complex data particular documents with ease and speed. Consequently, decision makers in any organization can generate possibilities in the analysis.

How document stores work

  • The data is indexed using the id field, and then people will index other fields manually.
  • The router is scaled to accept more queries at the same time at the shard servers.
  • It has more power to aggregate data

Columnar stores: Redshift

In the past few decades, people have turned to these storage systems for big data which uses the columns. This method allows various capabilities like speed in data saving, retrieval, and analysis. A good example of this is Redshift system by Amazon.

How columnar store works

  • It performs their operations on the disk which immensely reduces the query time as a way of indexing.
  • This system will have one leading node which handles distribution and aggregation of the data.

Final word

The above three non-traditional data storage systems have their pros and cons. Most engineers may be in a dilemma on which to pick and which to leave. However, it’s evident that MongoDB is a good approach to data with schema while elasticsearch is a pro at indexing. On the same note, Redshift will work well on column data.