To advance their AWS migration strategy, our client launched a tender to find the best IT service provider for this mission. Recognized for skilled AWS-certified engineers, many successful projects, and profound experience in building telecom solutions<\/a>, Intellias remarkably stood out among the competitors. Also, our AWS partner status attested to the fact that we meet strict requirements for AWS migration services in terms of expertise, capabilities, and engagement in the AWS cloud ecosystem.<\/p>\n Within the two-month duration of this PoC, we nimbly designed a customized cloud solution<\/a> architecture, assessed resource requirements, measured the performance of the developed systems, and calculated expected infrastructure costs. This allowed our client to validate their ideas and estimate implementation costs quickly and with minimal effort to further inform their decisions and help them plan properly for the actual migration to AWS. Like many telecommunications providers, our client has been relying on an on-premises database for their data processing and BI tools. Their existing data warehouse had extended exponentially, skyrocketing the cost of its maintenance and scalability due to more and more vendor licenses and hardware. The telecom provider thus decided to explore migration to AWS as a cost-saving and resource-efficient measure.<\/p>\n Along with cost and resource optimization, our client\u2019s motivation behind AWS cloud migration was to accelerate their growth and innovation and thus future-proof their position in the telecom industry. That\u2019s why we also worked to increase our client\u2019s long-term business value by upgrading their underlying big data and analytics<\/a> with the latest technologies such as data lakes and fast data.<\/p>\n In terms of our AWS migration services, we had to implement three dataset scenarios for our client and evaluate them based on cost and performance efficiency:<\/p>\n We came up with two architecture designs for each scenario. One uses custom jobs that explicitly replicate the functionality of the on-premises data warehouse in the cloud, and the other is entirely based on out-of-the-box AWS capabilities. As part of our AWS data migration service, we also estimated monthly expenses for cloud infrastructure<\/a> based on the volume of data handled by the telecom provider in real life. This flexible approach allowed our client to understand available options and make well-grounded choices based on the benchmark comparisons we provided.<\/p>\n To effectively implement batch processing<\/b> during the migration to AWS, we first measured relevant performance metrics for the existing on-premises data warehouse. After that, we transferred the data model to cloud components and designed an efficient extract\u2013transform\u2013load (ETL) solution that scored much better than the existing system. As most of the processing happened at night (when it\u2019s less expensive), only some critical jobs needed to run during the more expensive daytime hours. Therefore, we saved a lot of resources by not needing to keep all the infrastructure constantly engaged.<\/p>\n To enable predictive model<\/b> building when migrating to AWS, we prepared comprehensive data lake-based solutions, unlocking a full range of ML\/AI capabilities<\/a> for our client\u2019s data scientists and BI specialists. We focused on ensuring a seamless workflow so end users could build and run their models with improved speed and flexibility.<\/p>\n For stream data indexing<\/b>, we designed another ETL solution to implement fast searching by customer ID to instantly find information on the latest activity. As with batch processing, we first broke down the existing on-premises workflow into elements, measured the performance of each, and then decomposed the data model in AWS to achieve an optimized implementation. In this way, we effectively used fast data analytics<\/a> to promptly get actionable insights from an enormous data pool.<\/p>\n This business-critical project involved three stakeholders \u2014 our client (the largest national telecommunications provider), AWS (as a cloud platform provider), and Intellias (as an AWS data migration service consultant). We orchestrated our collaboration to make it mutually beneficial. A manager, solution architect, DevOps engineer, and data engineer from Intellias extended our client\u2019s team by providing technology skills in migrating to AWS.<\/p>\n The Intellias\u2013AWS partnership<\/a> has allowed us to have on-demand technical sessions and workshops with AWS subject matter experts to advise on best practices and eliminate risks. An excellent example of the data migration service AWS and Intellias have provided in tandem is effectively handling credit activation for our client.<\/p>\n Intellias took an active part in setting up the AWS cloud migration environment jointly with our client\u2019s team and helped them solve other technical issues such as adjusting the VPN transfer rate. We also conducted a detailed cybersecurity<\/a> analysis as part of our AWS migration services to ensure reliable protection against virtual threats for a safe cloud environment. The suggested approach would provide security across all network layers for our client \u2014 an essential requirement in today\u2019s telecom industry.<\/p>\n![\"AWS](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2021\/06\/kyivstar-aws-body-im.jpg\")
<\/p>\nSolution delivered<\/h2>\n
Holistic vision<\/h3>\n
Multipronged approach<\/h3>\n
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Expert synergy<\/h3>\n
Business outcome<\/h2>\n