1:"$Sreact.fragment"
2:I[3327,[],""]
3:I[7987,[],""]
4:I[9430,["430","static/chunks/430-3e18fa6ca90d78cd.js","345","static/chunks/app/not-found-a37cccdde72e5761.js"],""]
5:I[5312,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"AppContextProvider"]
6:I[5198,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"default"]
7:I[3141,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"default"]
8:I[949,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"default"]
9:I[3745,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"default"]
a:I[8093,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"default"]
b:I[6984,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"default"]
c:I[788,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","95","static/chunks/95-c3c40aa5017f7140.js","76","static/chunks/app/(main)/layout-2c6f19cab5401daf.js"],"default"]
e:I[6253,[],"OutletBoundary"]
10:I[5883,[],"AsyncMetadataOutlet"]
12:I[6253,[],"ViewportBoundary"]
14:I[6253,[],"MetadataBoundary"]
15:"$Sreact.suspense"
17:I[7077,[],""]
:HL["/_next/static/media/7b0b24f36b1a6d0b-s.p.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
:HL["/_next/static/css/466de881f30bd7cb.css","style"]
:HL["/_next/static/css/3bff27290ed8abbd.css","style"]
0:{"P":null,"b":"qQuEFBCw_Ffe8Nd4r6Bz1","p":"","c":["","projects","agency"],"i":false,"f":[[["",{"children":["(main)",{"children":["projects",{"children":[["slug","agency","d"],{"children":["__PAGE__",{}]}]}]}]},"$undefined","$undefined",true],["",["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/466de881f30bd7cb.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]],["$","html",null,{"lang":"en","children":["$","body",null,{"className":"font-sans antialiased __variable_d67418","children":["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","section",null,{"className":"section-container max-w-xl min-h-screen flex flex-col justify-center items-center gap-4 text-center","children":[["$","h1",null,{"className":"text-3xl sm:text-5xl md:text-6xl lg:text-7xl xl:text-8xl font-black uppercase tracking-tight text-center text-gray-800","children":"404"}],["$","p",null,{"children":"Found a dead end. Looks like you lost your way. The page you're looking for might have moved or doesn't exist."}],["$","$L4",null,{"className":"underline","href":"/","children":"Take me home"}]]}],[]],"forbidden":"$undefined","unauthorized":"$undefined"}]}]}]]}],{"children":["(main)",["$","$1","c",{"children":[[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/3bff27290ed8abbd.css","precedence":"next","crossOrigin":"$undefined","nonce":"$undefined"}]],["$","html",null,{"lang":"en","children":["$","body",null,{"className":"font-sans antialiased __variable_d67418","children":["$","$L5",null,{"children":[["$","$L6",null,{"children":[["$","$L7",null,{}],["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}],["$","$L8",null,{}]]}],["$","$L9",null,{}],["$","$La",null,{"className":"fixed inset-0 z-10 w-screen h-screen"}],["$","$Lb",null,{"className":"fixed inset-0 z-10 w-screen h-screen"}],["$","$Lc",null,{}]]}]}]}]]}],{"children":["projects",["$","$1","c",{"children":[null,["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]]}],{"children":[["slug","agency","d"],["$","$1","c",{"children":[null,["$","$L2",null,{"parallelRouterKey":"children","error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L3",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","forbidden":"$undefined","unauthorized":"$undefined"}]]}],{"children":["__PAGE__",["$","$1","c",{"children":["$Ld",null,["$","$Le",null,{"children":["$Lf",["$","$L10",null,{"promise":"$@11"}]]}]]}],{},null,false]},null,false]},null,false]},null,false]},null,false],["$","$1","h",{"children":[null,[["$","$L12",null,{"children":"$L13"}],["$","meta",null,{"name":"next-size-adjust","content":""}]],["$","$L14",null,{"children":["$","div",null,{"hidden":true,"children":["$","$15",null,{"fallback":null,"children":"$L16"}]}]}]]}],false]],"m":"$undefined","G":["$17",[]],"s":false,"S":true}
13:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]]
f:null
18:I[4419,[],"IconMark"]
11:{"metadata":[["$","title","0",{"children":"Financial Data Hub | Naveen Kalluri"}],["$","meta","1",{"name":"description","content":"Enterprise financial data consolidation platform achieving 20% storage cost reduction and 30% compute optimization"}],["$","link","2",{"rel":"icon","href":"/favicon.ico","type":"image/x-icon","sizes":"256x256"}],["$","$L18","3",{}]],"error":null,"digest":"$undefined"}
16:"$11:metadata"
19:I[6340,["340","static/chunks/0b0944fb-1f75ba373df7747e.js","430","static/chunks/430-3e18fa6ca90d78cd.js","758","static/chunks/758-2faeec29345bc6b9.js","128","static/chunks/128-6f3cb5aa184e5eba.js","904","static/chunks/904-e9f8c5daee6a6f1c.js","210","static/chunks/app/(main)/projects/%5Bslug%5D/page-2c9a97948f0ea147.js"],"default"]
d:["$","$L19",null,{"project":{"slug":"agency","content":[["$","h2",null,{"children":"Project Overview"}],"\n",["$","p",null,{"children":"Built a comprehensive Financial Data Hub at SMBC that consolidated data from multiple legacy source systems into a unified, single source of truth. This enterprise-scale data warehouse serves as the backbone for downstream applications across the organization, enabling real-time financial reporting and analytics."}],"\n",["$","p",null,{"children":"The platform was engineered using Azure cloud stack with Databricks as the core processing engine, implementing advanced parallelism and optimization techniques that resulted in significant cost savings—20% reduction in storage costs and 30% reduction in compute costs—while dramatically improving data accessibility and reliability."}],"\n",["$","h2",null,{"children":"Key Features"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Unified Data Platform"}],": Consolidated disparate financial data sources into a single, authoritative data repository"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Advanced Optimization"}],": Implemented parallelism techniques and Spark optimizations for 30% performance improvement"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Cost Efficiency"}],": Achieved 20% storage cost reduction through intelligent data partitioning and compression"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Scalable Architecture"}],": Designed for horizontal scaling to handle growing data volumes"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Real-time Processing"}],": Enabled near real-time data ingestion and transformation pipelines"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Data Governance"}],": Implemented Unity Catalog for comprehensive data cataloging and access control"]}],"\n"]}],"\n",["$","h2",null,{"children":"Technologies Used"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Databricks"}],": Core data processing platform with notebook-based development"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Azure Data Factory (ADF)"}],": Orchestration and workflow management"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"PySpark"}],": Large-scale data processing and transformations"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Spark SQL"}],": Complex data queries and aggregations"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Delta Lake"}],": ACID transactions and versioned data storage"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Azure Data Lake Gen2"}],": Scalable data storage layer"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Unity Catalog"}],": Data governance and cataloging"]}],"\n"]}],"\n",["$","h2",null,{"children":"Technical Implementation"}],"\n",["$","p",null,{"children":"The solution architecture followed a medallion approach with bronze, silver, and gold layers. Data ingestion was orchestrated through Azure Data Factory pipelines, with Databricks handling all transformation logic."}],"\n",["$","p",null,{"children":"Spark DataFrames and SQL were used extensively for complex table-to-table transformations, while PySpark scripting enabled custom business logic implementation. The implementation of Delta Live Tables ensured data quality and lineage tracking throughout the pipeline."}],"\n",["$","h2",null,{"children":"Challenges & Solutions"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Legacy System Integration"}],": Designed robust connectors for multiple legacy systems with varying data formats and update frequencies"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Performance Bottlenecks"}],": Implemented dynamic partition pruning and optimized join strategies to improve query performance by 30%"]}],"\n","$L1a","\n","$L1b","\n"]}],"\n","$L1c","\n","$L1d","\n","$L1e","\n","$L1f","\n","$L20"],"data":{"title":"Financial Data Hub","description":"Enterprise financial data consolidation platform achieving 20% storage cost reduction and 30% compute optimization","date":"2024-07-15","type":"Data Architecture","services":["Data Engineering","Cloud Architecture","Performance Optimization"],"role":"Azure Data Engineer","tags":["Databricks","Azure Data Factory","PySpark","Azure Data Lake","Delta Lake","Unity Catalog","Spark SQL"],"url":"","image":{"src":"/media/agency/featured.png","width":1440,"height":1024},"color":"#0078d4","featured":true}},"prevProject":null,"nextProject":{"slug":"fahri-2025","data":{"title":"Insurance Data Warehousing Platform","description":"Medallion Architecture implementation achieving 40% cloud compute cost reduction through legacy code optimization","date":"2023-12-20","type":"Data Warehousing","services":["Data Engineering","Cloud Migration","Data Architecture"],"role":"Azure Data Engineer","tags":["Azure Cloud","Databricks","Medallion Architecture","Azure Data Lake","Azure Synapse","Azure Logic Apps","Azure DevOps","CI/CD"],"url":"","image":{"src":"/media/fahri-2025/featured.png","width":1440,"height":1024},"color":"#005a9e","featured":true}}}]
1a:["$","li",null,{"children":[["$","strong",null,{"children":"Cost Optimization"}],": Analyzed and optimized cluster configurations, implemented auto-scaling, and optimized data storage formats"]}]
1b:["$","li",null,{"children":[["$","strong",null,{"children":"Data Quality"}],": Created comprehensive audit, balance, and control (ABC) frameworks using SQL database audit tables"]}]
1c:["$","h2",null,{"children":"Business Impact"}]
1d:["$","p",null,{"children":"The Financial Data Hub transformed how SMBC manages and analyzes financial data. By creating a single source of truth, the organization gained:"}]
1e:["$","ul",null,{"children":["\n",["$","li",null,{"children":"Faster decision-making through improved data accessibility"}],"\n",["$","li",null,{"children":"Significant cost savings through optimized infrastructure"}],"\n",["$","li",null,{"children":"Enhanced data quality and consistency"}],"\n",["$","li",null,{"children":"Improved regulatory compliance and audit capabilities"}],"\n",["$","li",null,{"children":"Foundation for advanced analytics and machine learning initiatives"}],"\n"]}]
1f:["$","h2",null,{"children":"My Role"}]
20:["$","p",null,{"children":"As the lead Azure Data Engineer, I was responsible for the complete solution architecture, implementation of ETL pipelines, Spark optimization, and performance tuning. I worked closely with business stakeholders to translate requirements into technical solutions and ensured successful deployment and knowledge transfer."}]