Fetching Polyglot Data is Cool, but How About Persistence?

Fetching Polyglot Data is Cool, but How About Persistence?

Fetching Polyglot Data is Cool, but How About Persistence?

The term “big data” refers to a large volume of data. There are millions upon millions of records in this database. They originate from a variety of sources, with the famed three Vs — Volume, Velocity, and Variety – in mind. DigiXT manages volume and velocity using cutting-edge distributed computing frameworks. We utilize templating to configure a number of data sources, including structured, unstructured, APIs, and real-time data. This addresses one of the critical requirements for managing ingestion from businesses that employ a multitude of distinct data storage solutions for various types of data.

While there are various extract-transform-load (ETL) technologies and platforms that can acquire polyglot data sources, there are only a few data platforms in the business that can support “polyglot” data lakes. DigiXT is thrilled to take a huge step forward in the world of data platforms by offering multi-format storage depending on the type of use cases.

Shifting perspectives of Data Lakes

We aim to shift people’s perceptions about how data lakes are used. To begin with, a data lake is not simply a repository for all data that is utilised for “only one purpose.” Let’s take a look at what a company’s transactional data repositories are like:


Figure 1 Transactional Data Stores: Many Types


Given the aforementioned circumstances, it’s natural to hypothesize that a data lake will not store all of the data in one format, given only a specific use case. We should build the data lake(s) to accommodate numerous use cases, just as we adopted transactional data storage for multiple reasons. That implies there has to be flexibility in the forms in which we may store data in the data lake, and it needs to be configurable. This is one of the DigiXT data platform’s key architectural tenets.


Polyglot Persistence in DigiXT

The primary objective of a data lake is to meet a range of consumer demands rather than simply storing data in one location. With the emergence of various data requirements such as artificial intelligence and machine learning, vast amounts of data must be fed for model training and development. As a result, storing the data in an appropriate format is critical. When we do this, it is very likely that some other use case may need the search of data. Another use-case is to load data quickly and then utilize it for aggregated consumer insights. We might not be able to attain performance for all of these use cases with a single data format and storage. But what about caching frequently used data? A key-value formatted storage system might be useful in this case.

Here is our solution:


Figure 2 DigiXT’s polyglot persistent Data Lake Approach


Unlike other data platforms in the market, we are adopting an innovative approach to keeping storage configurable based on use cases. We carefully design the data elements so that they are not duplicated. All individual abstractions are subsequently stored in a common storage layer backed by object storage technology. The data is encrypted, and stringent policies govern the access. Only appropriate authentication and permission allow external users and apps access to the layer. We also provide an MPP-based distributed query engine for retrieving data using basic SQL. Access, users, queries, and performance are all thoroughly inspected. The query engine may be coupled with the organization’s security infrastructure, such as LDAP, Okta, or OpenID.Standard access drivers are provided for dashboarding and reporting applications.


Following are the advantages

  1. Extensible for new kinds of evolving high-performance storage
  2. Not tied up with a vendor-proprietary format, so no lock-in
  3. Maximum performance benefits and ROI for the use case under consideration
  4. Concurrency benefits, since everyone is not accessing same storage format
  5. Since underlying storage for almost all formats is same (Object storage), disaster recovery and fault tolerance are easy to manage
  6. Scaling and distributing base storage is easy and manageable
  7. Single access point for all data, that too with simple SQL 92 standard

For more details, connect with us.