The immense volume of existing data creates significant data management challenges. The incoming deluge of even more data makes gaining control of it all an almost overwhelming challenge, which is why MongoDB is investing so heavily in flexible data management tools.
The newly released MongoDB 4.2 presents a flexible, schema-less suite of database management tools that will help your enterprise absorb, organize, and maximize the value of your current and future data stores.
Data, Data Everywhere
The digital universe continues to expand with more data – and more data types – emerging every day. That data both encompasses virtually all known information and acts as the foundation for evolving and new information. Researchers use what they know to invent new knowledge, then share that knowledge, too. The consequence is a vast existing store of data that is growing at an astonishing pace and raising the ‘how to control it’ bar for data management professionals. It’s estimated that the global volume of data will hit 175 zettabytes – one trillion GBs – by the year 2025. By contrast, the total amount of data was calculated at just 5 GB in 2002, less than 20 years ago.
Gaining Control Over the Uncontrollable
For digital technologists, containing the sheer volume of data is just one challenge. How to manage it and gain as much value from it as possible are two related challenges for which global engineers and developers are struggling to find adequate responses.
One reason for the challenge is the growing number of sources from which data flows. Not only does it emerge from neatly organized servers, data centers, and the cloud, but now data also comes in from intermediate sources – cell towers, subsidiary stations, etc. – and the widening Internet of Things (IoT). The IoT devices that are endpoints for so many of today’s digital systems generate reams of streaming data into data storage assets. There are literally millions of those in use right now, and millions more on the way.
All those unique data types pose an existential threat to standard data management tools. Traditional ‘relational’ data storage and management tools are based on related ‘schemas,’ and use a ‘table-column-row’ strategy to organize data and make it easily accessible. Keys connect the data from one column to that of another, and programmers are responsible for both creating and maintaining those keys as the database evolves.
However, structured schemas are simply not able to master either the volume or the complexity of the vast quantity and disparity of emerging data types. Unstructured data that doesn’t parse into neat columns and rows can’t be stored or used in a schema-based database because too much of it is inaccessible. Companies that receive unstructured data struggle to store it in a way that allows relatively easy access as well as usage to meet evolving corporate goals.
Simply put: today’s and tomorrow’s data types require a more flexible storage and access capacity if their value is to be maximized to its fullest potential.
MongoDB 4.2 Provides Flexible Data Management Solutions
MongoDB has been using non-schema’d data storage methodology (Non-Relational) for years because it provides developers with the flexibility they need to organize non-standard data types. Its non-relational organization gathers data into business usage ‘documents,’ not separate segments of related activities. Instead of dividing information into a column for customers, for example, and a column for sales and then linking the two, the non-schema’d organization gangs all the sales data related to a single customer into a single document so users can access the relevance of that customer by accessing that single document. Groups of documents are ‘collections’ connected by keys that relate relevant information across the documents in that collection.
Consequently, the MongoDB 4.2 database eliminates many of the challenges posed by structured data management tools:
- It doesn’t require the ‘normalization’ of non-standard data formats into standard data storage columns and rows, but stores the information into logically relevant documents.
- Wildcard indexing permits the inclusion of unknown resources into searches because its filter will match any subdocument, field or array within a collection without the need for those assets to be parsed out in separate indexes.
- Key values can be changed as needs require, such as sharing documents or collections across shards, for example.
The flow of data is only getting bigger and managing it well is getting harder. Datavail uses MongoDB 4.2 to embrace the flexibility of today’s emerging data types, formats and flow, and can help your organization master its unstructured data so it can maintain its market share well into the future.
A Faster Future with Newly Released MongoDB 4.2
This Datavail white paper explores new features introduced in the newly released MongoDB 4.2.
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Most people will encounter this error when their application tries to connect to an Oracle database service, but it can also be raised by one database instance trying to connect to another database service via a database link.