{"id":2168,"date":"2024-12-09T01:57:38","date_gmt":"2024-12-09T01:57:38","guid":{"rendered":"http:\/\/staging.diggibyte.com\/Diggibyte_57\/?page_id=2168"},"modified":"2025-06-03T08:59:46","modified_gmt":"2025-06-03T08:59:46","slug":"telecom-case-study","status":"publish","type":"page","link":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/telecom-case-study\/","title":{"rendered":"Telecom Case Study"},"content":{"rendered":"\t\t
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Telecom Case Study: Modernizing Data Pipelines with Databricks<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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One of the World\u2019s Largest Telecom Provider<\/h3><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\tMigration of Data Pipelines\u200b \t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Enterprise Data Platform Migration to Databricks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The client’s existing data platform was outdated, with data confined to a traditional Data Warehouse. This setup hindered the scalability of data pipelines and posed challenges in data governance, impacting essential business operations.\u200b<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Solution: Enterprise Data Platform Migration to Databricks<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The client transitioned to a Lakehouse Architecture using Databricks, achieving unified governance, improved scalability, and cost efficiency for various data and analytics workloads.\u200b<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tStored procedures \nconverted to Spark \nApplications\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Technology Transformation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  • Relied on an outdated platform with data residing in a traditional Data Warehouse.\u200b<\/li>
  • Faced challenges in scaling data pipelines and governing data effectively.\u200b<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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    • Implemented a Lakehouse Architecture using Databricks, providing unified governance and improved scalability.\u200b<\/li>
    • Achieved cost efficiency for various data and analytics workloads.\u200b<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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      Business Challenge<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      The client managed over 200 stored procedures on a platform characterized by data silos and redundant code, leading to increased operational costs due to a lack of governance and high maintenance requirements.\u200b<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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      Impact<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      The migration to Databricks Lakehouse resulted in:\u200b<\/p>

      • 30%year-on-year infrastructure cost savings.\u200b<\/li>
      • 25%reduction in duplicate data.\u200b<\/li>
      • 50%of stored procedures converted to Spark applications.\u200b<\/li><\/ul>

        This modernization effort led to a 40% reduction in infrastructure costs, elimination of over 50% of duplicate data, and standardization of the ETL framework.\u200b<\/p>

        By leveraging Databricks, the client achieved a more efficient, scalable, and governed data platform, aligning with their business objectives and technological advancements.\u200b<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

        Telecom Case Study: Modernizing Data Pipelines with Databricks One of the World\u2019s Largest Telecom Provider Use Case Migration of Data Pipelines\u200b Geography Nordic Region Industry Telecommunications\u200b Enterprise Data Platform Migration to Databricks Problem The client’s existing data platform was outdated, with data confined to a traditional Data Warehouse. This setup hindered the scalability of data […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":""},"class_list":["post-2168","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/pages\/2168","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/comments?post=2168"}],"version-history":[{"count":43,"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/pages\/2168\/revisions"}],"predecessor-version":[{"id":10596,"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/pages\/2168\/revisions\/10596"}],"wp:attachment":[{"href":"https:\/\/staging.diggibyte.com\/Diggibyte_57\/wp-json\/wp\/v2\/media?parent=2168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}