Automotive, Finance, and Manufacturing<\/h1><\/div>Smoothly driving efficiency and profitability in automotive and finance.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
0<\/span><\/div>Minutes reduction in run time <\/div><\/div><\/div><\/div><\/div><\/div>![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/lower-resource-consumption-and-costs-.webp\" "\\"lower")
<\/span>Resource consumption and costs<\/div><\/div><\/div><\/div><\/div><\/div>![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/minimized-human-error-and-maintenance.webp\" "\\"minimized")
<\/span>Human error and maintenance <\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Meet Our Client<\/h2><\/div>Accelerating digital transformation in automotive, finance, and manufacturing<\/strong><\/h3><\/div>Our client harnesses 100+ years of domain experience to deliver innovative solutions to real-world business challenges in the automotive, financial services, and manufacturing domains. Headquartered in Singapore, with a presence across ASEAN countries and South Asia, they offer proven AI\/ML-driven solutions that help their global customers redefine and accelerate their time-to-market.<\/p>\n<\/div><\/div><\/div>
Fragmented data landscape and workflow bottlenecks<\/h2><\/div>The business faced a challenge with disparate source systems for various loan portfolios, such as digital products, three-wheelers, and two-wheelers, each operating independently. This fragmented data landscape led to inefficiencies in integration and prolonged pipeline runtimes, hindering timely insights and operational agility. The need was to efficiently consolidate these sources, streamline data pipelines, and significantly reduce processing time to support faster decision-making and improved business outcomes.<\/p>\n
In addition, optimizing the AWS daily pipeline revealed several challenges such as workflow bottlenecks causing delays, excessive resource consumption with continuous jobs, and a lack of flexibility in on-demand processing.<\/p>\n<\/div><\/div><\/div>
Challenges<\/h2><\/div><\/i><\/div>Moving Mountains of Data<\/strong><\/p><\/div><\/div>\nThe client’s product generates comprehensive property reports including the final value of the property by integrating data from disparate sources.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Manual efforts and costly data<\/strong><\/p><\/div><\/div>\nFirst, data extraction from such diverse sources manually posed a huge challenge, as many of these sources needed to be purchased at a hefty cost, and consolidating such vast amounts of data was nearly impossible.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Wasted time and new errors<\/strong><\/p><\/div><\/div>\nSecond, the cognitive complexity defects identified by the SonarCube quality inspection tool created challenges for new developers, making it difficult for them to grasp the business logic and leading to additional time delays. This situation contributed to the introduction of new errors as well.<\/p>\n<\/div><\/div><\/div>
<\/div><\/div><\/div><\/div><\/div><\/div>Our team leveraged AI models to sieve through massive amounts of information and produce accurate results in real time. The use of LLMs also enabled us to simplify code and eliminate errors.<\/h2><\/div>Rajkumar Purushothaman,<\/strong>
\nHead \u2013 Activate Analytics<\/p>\n<\/div><\/div><\/div>![](\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%27620%27%20height%3D%27620%27%20viewBox%3D%270%200%20620%20620%27%3E%3Crect%20width%3D%27620%27%20height%3D%27620%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\")
<\/div><\/div><\/div><\/div>The Solution We Offered:<\/h1><\/div><\/i><\/div>Cutt down the run time of AWS daily pipeline<\/strong><\/p><\/div><\/div>By analyzing the existing AWS pipeline workflows, we identified bottlenecks that were causing delays. Implemented optimizations such as parallel processing and efficient resource allocation.<\/div><\/div><\/div><\/i><\/div>Changed all-day running jobs to on-demand jobs<\/strong><\/p><\/div><\/div>Zuci transitioned their data processing jobs from a continuous run model to an on-demand model. This involved re-architecting the job scheduling system to trigger jobs based on specific events or requests rather than running them continuously throughout the day.<\/div><\/div><\/div><\/i><\/div>Automated the pipeline end-to-end<\/strong><\/p><\/div><\/div>We spearheaded the automation of their data pipeline processes, integrating various stages from data ingestion to transformation and loading into target systems.<\/div><\/div><\/div><\/i><\/div>Integrated different pipelines of varying source systems into a single pipeline\u202f<\/strong><\/p><\/div><\/div>Our team led an initiative to consolidate multiple disparate data pipelines into a unified pipeline that could handle diverse source systems, including databases and flat files.<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Technology Stack<\/h2><\/div><\/div><\/div>![](\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%27200%27%20height%3D%27100%27%20viewBox%3D%270%200%20200%20100%27%3E%3Crect%20width%3D%27200%27%20height%3D%27100%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" "\\"pyspark\\"")
<\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/div><\/div>Business Impact<\/h2><\/div><\/div><\/div>![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/Business-Impact_1.webp\" "\\"Business")
<\/span><\/div>Cut down the run time of AWS daily pipeline by 45 minutes.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/Business-Impact_2.webp\" "\\"Business")
<\/span><\/div>Reduced resource consumption and costs associated with idle compute time.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/Business-Impact_3.webp\" "\\"Business")
<\/span><\/div>Minimized human error and enhanced the reliability of data delivery by automation of data pipelines.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/Business-Impact_4.webp\" "\\"Business")
<\/span><\/div>Reduced maintenance overhead, and simplified monitoring and troubleshooting efforts by the integration of different pipelines of varying source systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
We are happy to be a part of your real data-driven journey, let’s start here!<\/h3><\/div><\/div><\/div>
Smoothly driving efficiency and profitability in automotive and finance.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/span>Meet Our Client<\/h2><\/div>Accelerating digital transformation in automotive, finance, and manufacturing<\/strong><\/h3><\/div>Our client harnesses 100+ years of domain experience to deliver innovative solutions to real-world business challenges in the automotive, financial services, and manufacturing domains. Headquartered in Singapore, with a presence across ASEAN countries and South Asia, they offer proven AI\/ML-driven solutions that help their global customers redefine and accelerate their time-to-market.<\/p>\n<\/div><\/div><\/div>
Fragmented data landscape and workflow bottlenecks<\/h2><\/div>The business faced a challenge with disparate source systems for various loan portfolios, such as digital products, three-wheelers, and two-wheelers, each operating independently. This fragmented data landscape led to inefficiencies in integration and prolonged pipeline runtimes, hindering timely insights and operational agility. The need was to efficiently consolidate these sources, streamline data pipelines, and significantly reduce processing time to support faster decision-making and improved business outcomes.<\/p>\n
In addition, optimizing the AWS daily pipeline revealed several challenges such as workflow bottlenecks causing delays, excessive resource consumption with continuous jobs, and a lack of flexibility in on-demand processing.<\/p>\n<\/div><\/div><\/div>
Challenges<\/h2><\/div><\/i><\/div>Moving Mountains of Data<\/strong><\/p><\/div><\/div>\nThe client’s product generates comprehensive property reports including the final value of the property by integrating data from disparate sources.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Manual efforts and costly data<\/strong><\/p><\/div><\/div>\nFirst, data extraction from such diverse sources manually posed a huge challenge, as many of these sources needed to be purchased at a hefty cost, and consolidating such vast amounts of data was nearly impossible.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Wasted time and new errors<\/strong><\/p><\/div><\/div>\nSecond, the cognitive complexity defects identified by the SonarCube quality inspection tool created challenges for new developers, making it difficult for them to grasp the business logic and leading to additional time delays. This situation contributed to the introduction of new errors as well.<\/p>\n<\/div><\/div><\/div>
<\/div><\/div><\/div><\/div><\/div><\/div>Our team leveraged AI models to sieve through massive amounts of information and produce accurate results in real time. The use of LLMs also enabled us to simplify code and eliminate errors.<\/h2><\/div>Rajkumar Purushothaman,<\/strong>
\nHead \u2013 Activate Analytics<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>The Solution We Offered:<\/h1><\/div><\/i><\/div>Cutt down the run time of AWS daily pipeline<\/strong><\/p><\/div><\/div>By analyzing the existing AWS pipeline workflows, we identified bottlenecks that were causing delays. Implemented optimizations such as parallel processing and efficient resource allocation.<\/div><\/div><\/div><\/i><\/div>Changed all-day running jobs to on-demand jobs<\/strong><\/p><\/div><\/div>Zuci transitioned their data processing jobs from a continuous run model to an on-demand model. This involved re-architecting the job scheduling system to trigger jobs based on specific events or requests rather than running them continuously throughout the day.<\/div><\/div><\/div><\/i><\/div>Automated the pipeline end-to-end<\/strong><\/p><\/div><\/div>We spearheaded the automation of their data pipeline processes, integrating various stages from data ingestion to transformation and loading into target systems.<\/div><\/div><\/div><\/i><\/div>Integrated different pipelines of varying source systems into a single pipeline\u202f<\/strong><\/p><\/div><\/div>Our team led an initiative to consolidate multiple disparate data pipelines into a unified pipeline that could handle diverse source systems, including databases and flat files.<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Technology Stack<\/h2><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/div><\/div>Business Impact<\/h2><\/div><\/div><\/div><\/span><\/div>Cut down the run time of AWS daily pipeline by 45 minutes.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced resource consumption and costs associated with idle compute time.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Minimized human error and enhanced the reliability of data delivery by automation of data pipelines.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced maintenance overhead, and simplified monitoring and troubleshooting efforts by the integration of different pipelines of varying source systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
We are happy to be a part of your real data-driven journey, let’s start here!<\/h3><\/div><\/div><\/div>
Accelerating digital transformation in automotive, finance, and manufacturing<\/strong><\/h3><\/div>Our client harnesses 100+ years of domain experience to deliver innovative solutions to real-world business challenges in the automotive, financial services, and manufacturing domains. Headquartered in Singapore, with a presence across ASEAN countries and South Asia, they offer proven AI\/ML-driven solutions that help their global customers redefine and accelerate their time-to-market.<\/p>\n<\/div><\/div><\/div>
Fragmented data landscape and workflow bottlenecks<\/h2><\/div>The business faced a challenge with disparate source systems for various loan portfolios, such as digital products, three-wheelers, and two-wheelers, each operating independently. This fragmented data landscape led to inefficiencies in integration and prolonged pipeline runtimes, hindering timely insights and operational agility. The need was to efficiently consolidate these sources, streamline data pipelines, and significantly reduce processing time to support faster decision-making and improved business outcomes.<\/p>\n
In addition, optimizing the AWS daily pipeline revealed several challenges such as workflow bottlenecks causing delays, excessive resource consumption with continuous jobs, and a lack of flexibility in on-demand processing.<\/p>\n<\/div><\/div><\/div>
Challenges<\/h2><\/div><\/i><\/div>Moving Mountains of Data<\/strong><\/p><\/div><\/div>\nThe client’s product generates comprehensive property reports including the final value of the property by integrating data from disparate sources.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Manual efforts and costly data<\/strong><\/p><\/div><\/div>\nFirst, data extraction from such diverse sources manually posed a huge challenge, as many of these sources needed to be purchased at a hefty cost, and consolidating such vast amounts of data was nearly impossible.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Wasted time and new errors<\/strong><\/p><\/div><\/div>\nSecond, the cognitive complexity defects identified by the SonarCube quality inspection tool created challenges for new developers, making it difficult for them to grasp the business logic and leading to additional time delays. This situation contributed to the introduction of new errors as well.<\/p>\n<\/div><\/div><\/div>
<\/div><\/div><\/div><\/div><\/div><\/div>Our team leveraged AI models to sieve through massive amounts of information and produce accurate results in real time. The use of LLMs also enabled us to simplify code and eliminate errors.<\/h2><\/div>Rajkumar Purushothaman,<\/strong>
\nHead \u2013 Activate Analytics<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>The Solution We Offered:<\/h1><\/div><\/i><\/div>Cutt down the run time of AWS daily pipeline<\/strong><\/p><\/div><\/div>By analyzing the existing AWS pipeline workflows, we identified bottlenecks that were causing delays. Implemented optimizations such as parallel processing and efficient resource allocation.<\/div><\/div><\/div><\/i><\/div>Changed all-day running jobs to on-demand jobs<\/strong><\/p><\/div><\/div>Zuci transitioned their data processing jobs from a continuous run model to an on-demand model. This involved re-architecting the job scheduling system to trigger jobs based on specific events or requests rather than running them continuously throughout the day.<\/div><\/div><\/div><\/i><\/div>Automated the pipeline end-to-end<\/strong><\/p><\/div><\/div>We spearheaded the automation of their data pipeline processes, integrating various stages from data ingestion to transformation and loading into target systems.<\/div><\/div><\/div><\/i><\/div>Integrated different pipelines of varying source systems into a single pipeline\u202f<\/strong><\/p><\/div><\/div>Our team led an initiative to consolidate multiple disparate data pipelines into a unified pipeline that could handle diverse source systems, including databases and flat files.<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Technology Stack<\/h2><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/div><\/div>Business Impact<\/h2><\/div><\/div><\/div><\/span><\/div>Cut down the run time of AWS daily pipeline by 45 minutes.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced resource consumption and costs associated with idle compute time.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Minimized human error and enhanced the reliability of data delivery by automation of data pipelines.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced maintenance overhead, and simplified monitoring and troubleshooting efforts by the integration of different pipelines of varying source systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
We are happy to be a part of your real data-driven journey, let’s start here!<\/h3><\/div><\/div><\/div>
Our client harnesses 100+ years of domain experience to deliver innovative solutions to real-world business challenges in the automotive, financial services, and manufacturing domains. Headquartered in Singapore, with a presence across ASEAN countries and South Asia, they offer proven AI\/ML-driven solutions that help their global customers redefine and accelerate their time-to-market.<\/p>\n<\/div><\/div><\/div>
Fragmented data landscape and workflow bottlenecks<\/h2><\/div>The business faced a challenge with disparate source systems for various loan portfolios, such as digital products, three-wheelers, and two-wheelers, each operating independently. This fragmented data landscape led to inefficiencies in integration and prolonged pipeline runtimes, hindering timely insights and operational agility. The need was to efficiently consolidate these sources, streamline data pipelines, and significantly reduce processing time to support faster decision-making and improved business outcomes.<\/p>\n
In addition, optimizing the AWS daily pipeline revealed several challenges such as workflow bottlenecks causing delays, excessive resource consumption with continuous jobs, and a lack of flexibility in on-demand processing.<\/p>\n<\/div><\/div><\/div>
Challenges<\/h2><\/div><\/i><\/div>Moving Mountains of Data<\/strong><\/p><\/div><\/div>\nThe client’s product generates comprehensive property reports including the final value of the property by integrating data from disparate sources.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Manual efforts and costly data<\/strong><\/p><\/div><\/div>\nFirst, data extraction from such diverse sources manually posed a huge challenge, as many of these sources needed to be purchased at a hefty cost, and consolidating such vast amounts of data was nearly impossible.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Wasted time and new errors<\/strong><\/p><\/div><\/div>\nSecond, the cognitive complexity defects identified by the SonarCube quality inspection tool created challenges for new developers, making it difficult for them to grasp the business logic and leading to additional time delays. This situation contributed to the introduction of new errors as well.<\/p>\n<\/div><\/div><\/div>
<\/div><\/div><\/div><\/div><\/div><\/div>Our team leveraged AI models to sieve through massive amounts of information and produce accurate results in real time. The use of LLMs also enabled us to simplify code and eliminate errors.<\/h2><\/div>Rajkumar Purushothaman,<\/strong>
\nHead \u2013 Activate Analytics<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>The Solution We Offered:<\/h1><\/div><\/i><\/div>Cutt down the run time of AWS daily pipeline<\/strong><\/p><\/div><\/div>By analyzing the existing AWS pipeline workflows, we identified bottlenecks that were causing delays. Implemented optimizations such as parallel processing and efficient resource allocation.<\/div><\/div><\/div><\/i><\/div>Changed all-day running jobs to on-demand jobs<\/strong><\/p><\/div><\/div>Zuci transitioned their data processing jobs from a continuous run model to an on-demand model. This involved re-architecting the job scheduling system to trigger jobs based on specific events or requests rather than running them continuously throughout the day.<\/div><\/div><\/div><\/i><\/div>Automated the pipeline end-to-end<\/strong><\/p><\/div><\/div>We spearheaded the automation of their data pipeline processes, integrating various stages from data ingestion to transformation and loading into target systems.<\/div><\/div><\/div><\/i><\/div>Integrated different pipelines of varying source systems into a single pipeline\u202f<\/strong><\/p><\/div><\/div>Our team led an initiative to consolidate multiple disparate data pipelines into a unified pipeline that could handle diverse source systems, including databases and flat files.<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Technology Stack<\/h2><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/div><\/div>Business Impact<\/h2><\/div><\/div><\/div><\/span><\/div>Cut down the run time of AWS daily pipeline by 45 minutes.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced resource consumption and costs associated with idle compute time.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Minimized human error and enhanced the reliability of data delivery by automation of data pipelines.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced maintenance overhead, and simplified monitoring and troubleshooting efforts by the integration of different pipelines of varying source systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
We are happy to be a part of your real data-driven journey, let’s start here!<\/h3><\/div><\/div><\/div>
The business faced a challenge with disparate source systems for various loan portfolios, such as digital products, three-wheelers, and two-wheelers, each operating independently. This fragmented data landscape led to inefficiencies in integration and prolonged pipeline runtimes, hindering timely insights and operational agility. The need was to efficiently consolidate these sources, streamline data pipelines, and significantly reduce processing time to support faster decision-making and improved business outcomes.<\/p>\n
In addition, optimizing the AWS daily pipeline revealed several challenges such as workflow bottlenecks causing delays, excessive resource consumption with continuous jobs, and a lack of flexibility in on-demand processing.<\/p>\n<\/div><\/div><\/div>
Challenges<\/h2><\/div><\/i><\/div>Moving Mountains of Data<\/strong><\/p><\/div><\/div>\nThe client’s product generates comprehensive property reports including the final value of the property by integrating data from disparate sources.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Manual efforts and costly data<\/strong><\/p><\/div><\/div>\nFirst, data extraction from such diverse sources manually posed a huge challenge, as many of these sources needed to be purchased at a hefty cost, and consolidating such vast amounts of data was nearly impossible.<\/p>\n<\/div><\/div><\/div>
<\/i><\/div>Wasted time and new errors<\/strong><\/p><\/div><\/div>\nSecond, the cognitive complexity defects identified by the SonarCube quality inspection tool created challenges for new developers, making it difficult for them to grasp the business logic and leading to additional time delays. This situation contributed to the introduction of new errors as well.<\/p>\n<\/div><\/div><\/div>
<\/div><\/div><\/div><\/div><\/div><\/div>Our team leveraged AI models to sieve through massive amounts of information and produce accurate results in real time. The use of LLMs also enabled us to simplify code and eliminate errors.<\/h2><\/div>Rajkumar Purushothaman,<\/strong>
\nHead \u2013 Activate Analytics<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>The Solution We Offered:<\/h1><\/div><\/i><\/div>Cutt down the run time of AWS daily pipeline<\/strong><\/p><\/div><\/div>By analyzing the existing AWS pipeline workflows, we identified bottlenecks that were causing delays. Implemented optimizations such as parallel processing and efficient resource allocation.<\/div><\/div><\/div><\/i><\/div>Changed all-day running jobs to on-demand jobs<\/strong><\/p><\/div><\/div>Zuci transitioned their data processing jobs from a continuous run model to an on-demand model. This involved re-architecting the job scheduling system to trigger jobs based on specific events or requests rather than running them continuously throughout the day.<\/div><\/div><\/div><\/i><\/div>Automated the pipeline end-to-end<\/strong><\/p><\/div><\/div>We spearheaded the automation of their data pipeline processes, integrating various stages from data ingestion to transformation and loading into target systems.<\/div><\/div><\/div><\/i><\/div>Integrated different pipelines of varying source systems into a single pipeline\u202f<\/strong><\/p><\/div><\/div>Our team led an initiative to consolidate multiple disparate data pipelines into a unified pipeline that could handle diverse source systems, including databases and flat files.<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Technology Stack<\/h2><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/span><\/div><\/div><\/div><\/div><\/div>Business Impact<\/h2><\/div><\/div><\/div><\/span><\/div>Cut down the run time of AWS daily pipeline by 45 minutes.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced resource consumption and costs associated with idle compute time.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Minimized human error and enhanced the reliability of data delivery by automation of data pipelines.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div>Reduced maintenance overhead, and simplified monitoring and troubleshooting efforts by the integration of different pipelines of varying source systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
We are happy to be a part of your real data-driven journey, let’s start here!<\/h3><\/div><\/div><\/div>
Moving Mountains of Data<\/strong><\/p><\/div> The client’s product generates comprehensive property reports including the final value of the property by integrating data from disparate sources.<\/p>\n<\/div><\/div><\/div> Manual efforts and costly data<\/strong><\/p><\/div> First, data extraction from such diverse sources manually posed a huge challenge, as many of these sources needed to be purchased at a hefty cost, and consolidating such vast amounts of data was nearly impossible.<\/p>\n<\/div><\/div><\/div> Wasted time and new errors<\/strong><\/p><\/div> Second, the cognitive complexity defects identified by the SonarCube quality inspection tool created challenges for new developers, making it difficult for them to grasp the business logic and leading to additional time delays. This situation contributed to the introduction of new errors as well.<\/p>\n<\/div><\/div><\/div> Rajkumar Purushothaman,<\/strong> Cutt down the run time of AWS daily pipeline<\/strong><\/p><\/div> Changed all-day running jobs to on-demand jobs<\/strong><\/p><\/div> Automated the pipeline end-to-end<\/strong><\/p><\/div> Integrated different pipelines of varying source systems into a single pipeline\u202f<\/strong><\/p><\/div> Cut down the run time of AWS daily pipeline by 45 minutes.<\/p>\n<\/div><\/div><\/div> Reduced resource consumption and costs associated with idle compute time.<\/p>\n<\/div><\/div><\/div> Minimized human error and enhanced the reliability of data delivery by automation of data pipelines.<\/p>\n<\/div><\/div><\/div> Reduced maintenance overhead, and simplified monitoring and troubleshooting efforts by the integration of different pipelines of varying source systems.<\/p>\n<\/div><\/div><\/div><\/div><\/div>Our team leveraged AI models to sieve through massive amounts of information and produce accurate results in real time. The use of LLMs also enabled us to simplify code and eliminate errors.<\/h2><\/div>
\nHead \u2013 Activate Analytics<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>
The Solution We Offered:<\/h1><\/div>
Technology Stack<\/h2><\/div><\/div><\/div>
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/pyspark.webp\" "\\"pyspark\\"")
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/AWS-lambda.webp\" "\\"AWS")
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/AWS-glue.webp\" "\\"AWS")
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/AWS-api-gateway.webp\" "\\"AWS")
![](\"https:\/\/www.zucisystems.com\/wp-content\/uploads\/2024\/12\/Step-functions.webp\" "\\"Step")
Business Impact<\/h2><\/div><\/div><\/div>
<\/span><\/div><\/span><\/div><\/span><\/div><\/span><\/div>We are happy to be a part of your real data-driven journey, let’s start here!<\/h3><\/div><\/div><\/div>