Big Data integration as a service
Big Data analytics can be immensely useful to any business at some point — either as a compelling feature creating a competitive edge for your products or as a powerful instrument for optimizing infrastructure expenses. Either way, it is wise to assume your competitors already use Big Data tools and try to outperform them. However, this is not possible without an in-depth understanding of what Big Data is, how it works and how it can help your business.
Big Data is an umbrella term for a variety of workflows, practices and tools involved in the gathering and processing of huge volumes of various data in real-time. Unlike sampling used previously, it can process all the data available and highlight the patterns that could have been left completely unnoticed previously. However, in order to provide immense business benefits, your Big Data solutions must be designed, built and run correctly, Otherwise you risk spending lots of money on a project that delivers no value.
One of the main challenges of a correct Big Data implementation is transforming the whole range of processed data into a uniform and transparent format — usually as JSON files. This Big Data integration requires the ability to effectively deduplicate the incoming data, normalize it, remove the unneeded noise and prepare it for further usage as a training data set for a Machine Learning model. Doing this correctly from the start requires an in-depth knowledge of the correct scenarios, which must be based on previous successful projects.
The complexity of Big Data integration lies with the fact that various types of data require different tools to process them, but a Machine Learning model has to work with a single type of input to provide cohesive output. Your systems might provide a ton of machine-generated data like server logs, CRM database records, HTML response codes, media content, textual data and graphs. All of this must be transformed into a JSON format on the fly and rapidly processed, as Big Data has no value if it is available in a week — it must work right now.
Therefore, you need to place your Big Data analytics in the cloud to use huge capacities of CPU power and be able to scale your instances up and down to meet the changing requirements of your workloads. However, transferring all your data warehouses to the cloud for processing and storing the data there can be quite costly, not to mention it is limited by the throughput capacity of your server Ethernet cards.
Thus said, the best solution for Big Data analysis actually lies within configuring hybrid cloud solutions, where the data is stored and processed on bare-metal servers on-prem, while the computing power resides in the public or private cloud. Such solutions have proven their superiority time and again, as they possess all the security, flexibility and scalability of the cloud while ensuring cost-efficiency of your Big Data integration if we compare them to traditional Business Intelligence systems, which require on-prem datacenters to run.
Thus said, a certain level of Big Data design and DevOps configuration skills is required to make such systems operational, and hiring the specialists with relevant skills and experience in-house is quite hard. Most of such DevOps engineers and Big Data architects are employed either by global corporations, by cloud service providers like AWS or Google Cloud, or by Managed Services Providers like IT Svit.
Should you want to build and run a cost-efficient and reliable Big Data analytics solution providing seamless Big Data integration services — IT Svit is ready to help. Contact us right away with your project requirements and we will help your project succeed!
