{"id":2652,"date":"2021-11-16T19:29:28","date_gmt":"2021-11-16T19:29:28","guid":{"rendered":"https:\/\/bitsbytesoft.com\/?page_id=2652"},"modified":"2022-04-22T19:13:56","modified_gmt":"2022-04-22T19:13:56","slug":"aws-migration-services","status":"publish","type":"page","link":"https:\/\/bitsbytesoft.com\/aws-migration-services\/","title":{"rendered":"AWS Migration Services"},"content":{"rendered":"
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Case study<\/h3>\n\n

AWS Migration Services for Seamless Big Data Analytics in Telecom<\/h2>\n\n

We\u2019ve designed conceptual solutions to verify the AWS Migration Services strategy in terms of infrastructure cost efficiencies and advanced data analysis<\/strong><\/p><\/div><\/div>

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Business challenge<\/h2>\n\n

Our client is a major national telecommunications company providing mobile and broadband data transmission services to over 23 million subscribers. In search of ways to scale their business using modern solutions but at optimal cost, they needed to move away from on-premises servers and decided to evaluate the concept of AWS cloud migration. By migrating their existing data warehouse to AWS, our client envisioned streamlining their data processing and business intelligence (BI) systems. For this, they required a cloud technology partner offering AWS migration services to build a proof of concept (PoC) to verify the efficiency of such a transition.<\/p>\n\n

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\u00a0building telecom<\/a> solutions, BitsByteSoft remarkably stood out among the competitors. Also, our AWS partner status attested to the fact that we meet strict requirements for AWS migration services<\/a> in terms of expertise, capabilities, and engagement in the AWS cloud ecosystem.<\/p>\n\n

Within the two-month duration of this PoC, we nimbly designed a customized cloud solution 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.<\/p>\n\n

\"AWS<\/figure><\/div>\n\n

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Key features<\/h3>\n\n
\"Design<\/figure><\/div>\n\n

Design an architecture to migrate data processing and analytics from on-prem to the AWS cloud<\/span><\/strong><\/p>\n\n

\"Benchmark<\/figure><\/div>\n\n

Benchmark performance to optimize cloud infrastructure and use of services<\/span><\/strong><\/p>\n\n

\"Estimate<\/figure><\/div>\n\n

Estimate infrastructure costs at scale to ensure seamless and efficient business growth<\/span><\/strong><\/p>\n\n

Industry:<\/strong> Telecom<\/p>\n\n

Team size:<\/strong> 4 engineers<\/p>\n\n

Duration:<\/strong> 2 months<\/p>\n\n

Technologies:<\/h5>\n\n

CloudWatch \/ EMR \/ Glue \/ IAM \/ Kinesis \/ Kubernetes \/ Redshift \/ S3 \/ Site-to-Site VPN \/ Spark (pySpark) \/ VPC<\/p><\/div><\/div>

Solution delivered<\/h2>

Holistic vision<\/h3>

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>

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 with the latest technologies such as data lakes and fast data.<\/p>

Multipronged approach<\/h3>

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>