How Innoppl helped a leading healthcare software provider with a centralized Data Lake system

Innoppl’s expertise and revolutionary data lake system transformed the healthcare software provider’s data management practices. Thanks to this partnership, the client now enjoys enhanced operational efficiency, improved decision-making, and robust data security, all contributing to their continued success in the industry.

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The Result


Improved Efficiency


Reduced Operational Costs


Better Performance


The client is a prominent provider of healthcare software applications in the United States. Their customers utilize their application to gather and retain data for medication and patient analysis. However, their customers encountered an issue when trying to store a vast and varied dataset from multiple sources, including electronic health records, claims, lab results, patient surveys, and clinical trials. It posed a minor setback in the application’s functionality.

The Challenge

The client wanted to store, process, and analyze this data in a centralized and scalable way that could support their business needs and goals. They tried to incorporate advanced analytics directly into their application. The existing analytics system, based on individual customer databases, used complex SQL queries that were only sometimes applicable due to varying data models. Alterations were hampered by an outdated approach, demanding significant labor and testing, leading to performance issues. A scalable, easily integrated solution was needed to utilize the extensive data available for customers better.


They approached Innoppl’s data and analytics team since we specialize in creating custom solutions for healthcare organizations. We proposed building a centralized Data Lake system for the client using their expertise in cloud computing, big data technologies, and data engineering.

The solution was offered for two reasons:

1. A Data Lake is a type of data storage system that allows users to store raw and unstructured data in its native format without requiring any predefined schema or structure.

2. It can be easily accessed and queried by various tools and applications, enabling users to perform complex analytics and generate insights from the data.

Our data experts designed and developed the Data Lake system by following these steps:

Data ingestion: We used various methods to ingest the data from different sources into the Data Lake system. They used connectors to integrate with the client’s existing systems, such as EHRs, billing systems, lab systems, etc. They also used APIs to access external sources of data, such as social media platforms, research databases, etc. They ensured that the data was validated, cleansed, transformed, and enriched before loading into the Data Lake system.

Data storage: Our team used Amazon S3 as the underlying storage layer for the Data Lake system. Amazon S3 is a highly scalable and durable object storage service that can store any amount of data at any time. We configured Amazon S3 buckets according to the requirements of the client, such as security policies, access control lists (ACLs), encryption keys, etc. We also used AWS Glue as the metadata management service for the Data Lake system.

Data processing: We used Apache Spark as the core processing engine for the Data Lake system. Apache Spark is an open-source framework for distributed computing that can handle large-scale data processing tasks efficiently and scalably. We developed custom Spark applications using Scala programming language that could perform various operations on the data stored in Amazon S3 buckets, such as filtering, grouping, aggregating, joining, transforming, etc.

Data analysis: Our team used various tools and frameworks to analyze the processed data from the Data Lake system. We used Apache Hive as an interactive query engine that could run SQL-like queries on structured or semi-structured data stored in Amazon S3 buckets or other sources. They also used Apache Kafka as a streaming platform that could ingest real-time or near-real-time data from various sources into Amazon S3 buckets or other destinations. They also used Apache Flink as another streaming platform that could process high-throughput or low-latency streaming applications on distributed systems.

What Our Client Says

The client was thrilled with the solution provided by Innoppl’s data and analytics team. It helped us build a centralized Data Lake system that enabled them to handle various tasks like case management, authorizations, task tracking, and compliance reporting with ease. Their data management and analytics capabilities have improved since we implemented the Data Lake system. The security framework was overhauled for simplicity, using two key tables to ensure secure, two-way integration between the visualization tool and the SaaS application.

Results Obtained

The benefits that are derived from the solution offered by our Data experts.

  • Improved efficiency: The Data Lake system helps healthcare organizations store and access all their raw and unstructured data in one place without having to move or copy it across different systems or platforms. It can reduce their operational costs and improve their performance.
  • Enhanced flexibility: The system can help access and query the data using various tools and applications without having to worry about schema changes or compatibility issues. It can enable companies to explore different types of analytics and generate insights from their diverse data sources.
  • Increased scalability: The system can help healthcare organizations scale up or down their storage capacity according to their business needs without affecting their existing systems or applications. It can ensure that they have enough resources to handle their growing volume of data.
  • Advanced security: It can also help healthcare organizations secure their sensitive information using encryption keys, access control lists (ACLs), and other security measures and protect them from unauthorized access, data breaches, and cyberattacks.

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