AWS is a great platform for any UK-based business willing to leverage Big Data analytics. Aelius Venture provides dedicated teams that deliver IT outsourcing services and enable AWS Big Data services for your business.
Aelius Venture Helps Configure AWS Big Data Services for Your Business!
Many businesses want to use Amazon Web Services Big Data solutions to get the most out of their investments into cloud infrastructure optimization, workflow automation, and personalization of products and services. The main challenge here is that every Big Data platform is unique, so in-depth expertise in Big Data analytics is needed to configure these web services from AWS correctly. Aelius Venture has such expertise and can provide Big Data consulting, design, and implementation for your business!
AWS Big Data Competency to Empower Your IT Operations
Why not hire AWS support engineers & system administrators to manage Amazon Cloud Big Data for your company? AWS system engineers are experts in using Big Data web services AWS platform provides, yes — but every customer is just a ticket number for AWS support. Aelius Venture has nearly the same level of competency with AWS Big Data tools — but we are much smaller, and every customer is treated with due respect.
Designing & Running AWS Big Data Analytics for Your Business
Amazon Web Services provide a wide variety of powerful tools for Big Data analytics. However, each Big Data project is unique and cannot be built according to standardized guides. This is why the best way to ensure your Big Data solution is working correctly is by employing skilled software engineers who have already succeeded with a similar project. Aelius Venture can help with this challenge!
Aelius Venture Helps Get Big Data Amazon Web Services Working Right
AWS is the leading cloud service provider worldwide and it offers a huge variety of services, including Big Data solutions able to accomplish different tasks for different industries: business intelligence, predictive analytics, real-time streaming analytics, and more. If you are already with Amazon Web Services, you’ll want to utilize its offers to the fullest extent, including the structured and unstructured machine-generated data from your cloud infrastructure: server logs, social interactions of your customers, anonymized billing data, etc. This means you would need to build a data warehouse or a data lake using Amazon Redshift and enable data processing using Amazon Glacier, Amazon S3, and Amazon Glue.
However, you do need to understand which of these AWS Big Data tools are intended for which purpose. It is important because many businesses miss out on multiple revenue opportunities due to simply not realizing they already have it at their disposal.
First of all, what is Big Data? It is the umbrella term for a special kind of data and the practices of working with it. This kind of data has three distinctive parameters (actually four, as we will describe below):
Variety. Big Data works with a wide variety of data formats — web server logs, system journals, various kinds of records, multiple file extensions like .csv, .rtf, .txt, .docx, etc, images, and any other kind of data your project might operate with. This is ensured by Amazon S3 object storage, which helps store all kinds of information and organize it for further data processing.
Velocity. Big Data solutions can use streaming processing, batch processing, real-time data processing, etc. AWS web services provide the ability to work with data at scale, so instead of processing the data samples and extrapolating, you can process the whole entirety of available data in real time. Amazon CloudFront can handle the data collection, while Amazon Lake formation is used to ensure the data can be processed live.
Volume. Big Data is really big. Data sets can easily reach terabytes and petabytes in size, and all of this data must be cataloged, deduplicated, checked for consistency and completeness, white noise must be filtered out, etc, and the data must be stored securely. AWS Big Data storage can handle this task with the help of Amazon Glacier and Amazon Glue.
These components form the fourth parameter — value. The data you process must provide value to your business because the resources spent on processing it are spent in vain otherwise. Thus said, before even beginning the data processing, you must determine what purpose it must serve and how it will deliver value to your business or your customers.
For example, AWS Big Data tools can help you securely store large data sets of customer interaction data and analyze them to uncover hidden patterns. This will lead to enabling predictive analytics like the famous “customers who bought this also bought…” from the Amazon suggestion engine, which will help you with upselling and cross-selling to maximize your revenue potential.
Another example of getting value out of your Big Data is applying the Machine Learning models to build a holistic picture of your cloud infrastructure and determine normal operational parameters. This way, if these parameters change (for example, numbers of active app connections quickly grow from 200 to 5,000 a minute), the ML platform deploys one of the predefined response scenarios — like scaling the infrastructure up to meet the demand or diverting the incoming traffic to a CDN to negate the impact of a DDoS attack.
Scaling your instances up and down to meet regular workload spikes can be done using standard cloud web services, but this requires some schedule. Any workload aberrations happening at an unpredictable time are very hard to deal with. However, Machine Learning models work 24/7 and deal with the challenges quickly and efficiently whenever they appear.
Thus said, to enable this kind of efficiency, you must understand how Amazon Web Services work and how to configure their Big Data solutions correctly. Of course, you might choose the simplest way and opt for managed web services AWS provides, so you do not need to worry about underlying infrastructure and workflows at all. You simply specify the data sources, the expected results, and the convenient output destination, and AWS support engineers do the rest.
There are just 2 major downsides to this — high cost and vendor lock-in. AWS software development engineers will use AWS features and web services to build the Big Data analytics platform for you. These tools were designed to work cohesively with each other, so the system will be robust and resilient — but it will be quite costly, and it will work only with AWS. Should you decide to move to another public cloud platform or on-prem cloud — you will have to rebuild this Big Data analytics system from scratch.
Aelius Venture offers a good solution for this problem — outsourcing the AWS Big Data analytics services to our skilled specialists. We have in-depth AWS Big Data competency due to 7+ years of experience and more than 20 successful Big Data analysis projects deployed to the Amazon cloud. We know what AWS Big Data tools to use for any particular data processing project and how to configure it correctly.
What is even more important, however, is that we know what AWS-specific components can be safely replaced with free-to-use open-source alternatives. This way, we design, build and run cost-efficient, modular systems that can work equally well on any cloud platform. If this rings the bell for you — contact us right away and we will be glad to provide managed AWS Big Data services for you!