Understanding the Essentials of Big Data Databases

What Is Big Data?

It is data that is rapidly generated in increasingly large volumes and in a wide variety of data types. It is generated at a much faster pace, and it comes from many more sources than traditional data sets, which typically come from limited sources and in limited data types.

The three defining Vs of big data warehouses are:

• Volume – Individuals, companies, and organizations produce much higher volumes of data than ever before. This is because there are many more ways to produce data from multiple sources. Some examples of this include data from social media feeds, online transactions in e-commerce stores, and IoT (the Internet of Things) devices that collect and store data about equipment.
• Velocity – This refers to the rate at which data is generated, received, and acted upon by key decision-makers. These days, big data databases are powerful enough to process large amounts of big data at super-fast lightning speeds. In doing so, this allows for real-time (or near real-time) evaluation and action, allowing for faster, more informed decision-making.
• Variety – The variety of big data is much larger than traditional data sets. This is because big data comes in all kinds of data types. These include text, audio, video, images, geospatial data, and 3D-generated content. Different types of sources produce different types of big data. For instance, semi-structured data typically comes from mobile applications, emails, and IoT devices, as it still conforms to a structure without being restricted to fixed tables and columns.

Each type of big data requires a different set of tools and databases in order to be processed, analyzed, and acted upon. And if the evolution of big data has told us anything, the number of solutions will only grow bigger.

What Are Big Data Databases?

Big data databases are non-relational databases. They store data in a format other than relational tables. They are designed specifically to collect and process different big data types, including structured data, semi-structured data, and unstructured data. Unlike the data lake, which is a storage layer for data of any type, the big data database can bring structure to that data and make it queryable, being optimized for analytics.

Big data databases have a flexible schema. This means the fields don’t need to be the same, and each field can have different data types. They can also be horizontally scaled, as the workload can be distributed across multiple nodes. This is only possible with non-relational databases, as they’re self-contained and not connected relationally.

The four most common distributed database solutions are:

• Document databases store data in documents. In a non-relational database, a document is a record that stores information about an object and any related metadata. These documents store data in field-value pairs. The value of these pairs can be all kinds of types and structures, such as objects, strings, numbers, dates, and arrays.
• Key-value databases store data in a key-value format. To retrieve the value of a piece of data, one must type in the unique key or number associated with that value. Values can be basic objects like strings and numbers, or they can be more complicated objects.
• Wide-column stores store data in dynamic columns, which can be distributed across multiple nodes and servers. This means that, unlike a relational database, the names and formats of each column can vary with each row. And since data is stored in columns, finding a specific value in a column is very fast.
• Graph databases reserve data in nodes and edges. While nodes store identifiable information about an object, such as the name of a person or place, edges store information about the relationship between nodes.

What Are the Advantages of Big Data Databases?

There are many advantages to using big data databases for data science services. Big data tools can process the kind of complex data sets that relational databases cannot. They can also handle large volumes of different data formats across multiple sources. And thanks to their scale-out architecture, they can take full advantage of cloud and edge computing.

• Can store and process complex data sets – Big data technologies can manage a combination of structured, semi-structured, and unstructured data. This makes it easier for businesses and organizations to make sense of the data that they collect, as the data closely resembles how it appeared in the application that generated it.
• Easy to scale – Big data databases are better equipped to handle large volumes of disparate data than relational databases. This is because the storage and processing of data can be spread across multiple computers. The more data that is added, the more computers are added to handle the increasing demand.
• Cloud and edge computing – Big data databases were created with cloud and edge computing in mind. This allows businesses and organizations using big data databases to transfer some or all of their data processing to the cloud and the edge. In doing so, businesses and organizations can build, test, and deploy applications on a hybrid or multi-cloud model.

What Are the Disadvantages of Big Data Databases?

Despite the clear advantages of NoSQL databases, there are many big data challenges. The lack of standardization among big data databases can make them hard to set up and manage. Many big data databases also suffer from a lack of ACID (Atomicity, Consistency, Isolation, and Durability) support, which makes it harder to ensure that database transactions are processed correctly.

• Lack of standardization – Most NoSQL databases use either their own schemas or no schema at all. So, for businesses, organizations, and developers, understanding the strengths and weaknesses of each NoSQL database can be time-consuming, resulting in a lot of effort spent on pre-selection and integrating it into your existing workflow.
• Inconsistent ACID transactions support – ACID is a system of properties used by SQL databases to ensure proper online transaction processing. One such property is Atomicity. This ensures that in a multi-step process, such as transferring money from one bank account to another, the process is stopped if a problem occurs in any step. With the absence of properties like this, extra measures must be taken to ensure that the data produced by a NoSQL database is trustworthy.