
The Basics You Need to Know about Streaming Data Architecture
Building a strong streaming data architecture is what you need to survive and thrive in today’s explosive growth of data streams.
Big data databases collect, organize, and store large amounts of big data. The term big data is data that is huge in Volume (Size), Variety (Type), and Velocity (Speed), and it comes in structured data, semi-structured data, and unstructured data formats.
The main benefit of big data databases is that they can rapidly ingest and process petabytes (where one petabyte equals 1,024 terabytes) of data. They are also not confined to fixed tables and columns, so they can more efficiently process the kind of complex data sets that traditional SQL databases cannot process.
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:
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.
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:
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.
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.
There are many considerations to make when choosing a big data database. You should consider the size, type, and variety of the data you wish to collect. Other important considerations include security, compatibility with your existing systems, and the specific goals of your business or organization.
Here are a few useful tips to help you choose the right big data database.
What kind of data do you want to collect, and what do you want to do with it? If your plan is to collect data from multiple processes and microservices in an application, then use a key-value database, as they are great for storing data that does not have complex relationships or joints. However, if you want to reveal complex and hidden relationships between different data sets, then a graph database will help you identify those relationships and make smart business decisions.
Aside from choosing the right big data database solution, make sure the people you choose to develop and manage your database solution are right for the job. They should have relevant knowledge and experience in working with your desired big data database. Therefore, a deep understanding of building, testing, and maintaining data architecture is essential. They should also be familiar with programming languages and how to analyze big data.
By choosing a provider with strong communication skills, you will have an easier time expressing your needs, monitoring their progress, and understanding the insights they bring to you. The provider should be easy to understand in all the different forms of written and verbal communication, including text, email, video chat, and (if relevant) in-person meetings. Furthermore, they should be able to explain to you, in plain terms, the technology powering your big data database and the insights it is producing for you.
Big data databases are being used by businesses and organizations, big and small, around the world to better understand their products and services, their customers, and their processes. In doing so, they’re able to uncover insights previously inaccessible to them, enabling them to make faster, more informed business decisions.
If these are the kind of results you want for your business or organization, then partner with a trusted big data database solutions provider. They can help you define the goals of your business or organization, propose the right big data database for you, and then get to work building, deploying, and managing the solution for you.
And if you are looking for custom software outsourcing services for your big data database needs, contact us at Orient Software. We specialize in big data and can help you customize the right solution for your business or organization. With our dedicated team of experts behind us, we can design, build, deploy, and manage a custom-built big data database solution that meets your needs. Moreover, we have consultants with expertise in big data technologies, machine learning, artificial intelligence, and heaps more advanced technologies that can help you get the most out of your database solution. Get in touch with us today to learn more about how we can put big data to work for you.
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