Businesses today are rapidly modernizing their data infrastructure to handle big data more efficiently, automate workflows, and gain valuable insights. However, despite the investment in advanced data-related technologies, many organizations are often struggling to maximize returns from their modernized data platforms. This predicament stems from the architectural shortcomings in their data platforms, manifesting as scalability issues, operational complexities, and elevated maintenance overheads. To overcome this underperformance of their existing data platforms, making foundational changes in the data platform architecture becomes critical.
In this article, we take a closer look at data platform rearchitecting – exploring challenges that call for evaluating the fundamental components of an organization’s existing big data platform architecture and the benefits of a strategically architected data platform.
10 Reasons Why Organizations Fail to Get Maximum Value Out of Their Data Platform Architecture
Organizations often struggle to maximize returns from their present data infrastructure and platform due to the following limitations:
1. Lack of Alignment with Business Goals
2. Scalability Issues
3. Data Integration Challenges
4. Data Handling Incapabilities
5. Technical Rigidities
6. Slow ETL Processes
7. Lack of Support for Real-time Analytics
8. Data Monetization Challenges
9. Security and Compliance Concerns
10. Manual Dependencies
All these challenges and shortcomings can be overcome by revisiting the architecture of the existing data platform, ensuring that it aligns with strategic goals, meets high data quality standards, promotes user adoption, and capitalizes on the full potential of data assets.
How Rearchitecting Your Data Platform Can Meet All Your Data Requirements?
Let’s understand how the various data-related requirements and challenges can be addressed through sustainable improvements to the data platform architecture using the right rearchitecting approach.
Requirement 1: Ingesting Varied Data Formats from Multiple Sources
The foundation of any big data solution lies in one or more data sources, which can include application data stores, such as relational databases, static files generated by applications such as web server log files, and real-time data sources like IoT devices. The data ingestion layer serves as the vital link connecting source systems that produce raw data to the overarching data solution.
Solution 1: Introducing a more flexible and scalable data ingestion layer.
In instances where an organization experiences challenges in ingesting and managing a variety of data sources with different formats and structures, rearchitecting can introduce a more flexible and scalable data ingestion layer. This enhancement can seamlessly connect the source systems and enable an efficient flow of diverse data types to support modern data processing and analytics demands.
Requirement 2: Storing Large Volumes of Diverse Datasets
A distributed file store or a data lake is commonly employed for storing large volumes of diverse datasets for batch processing operations. Organizations may experience scalability issues with their storage system, let’s say a monolithic platform, may not be able to efficiently handle growing data volumes.
Solution 2: Shifting to a microservices-based storage solution and leveraging cloud-based data lakes or distributed file stores.
Examples of storage solutions include Azure Data Lake Store or blob containers in Azure Storage. In on-premises or hybrid environments, the Hadoop Distributed File System (HDFS) is widely adopted for storing and processing extensive datasets within the Apache Hadoop framework. Besides serverless data platforms, such as Amazon S3 and Google BigQuery which facilitate efficient storage and querying of large datasets, and containerized data solutions such as Kubernetes represent other innovative approaches to building flexible and scalable data storage.
Requirement 3: Efficient Batch Data Processing
The filtering, aggregation, and data preparation for analysis in the case of large datasets is done using long-running batch jobs. Prolonged execution time and suboptimal resource utilization are common in batch processing operations of large data sets.
Solution 3: Adopting more agile, scalable, and efficient processing approaches.
These can include adopting serverless computing, containerization for portability, utilizing scalable frameworks like Apache Spark for processing, and implementing managed data processing services.
Technologies such as U-SQL jobs in Azure Data Lake Analytics, Hive, Pig, custom Map/Reduce jobs in an HDInsight Hadoop cluster, or programming languages like Java, Scala, or Python in an HDInsight Spark cluster can read source files, process them, and output the results to new files faster.
Requirement 4: Real-time Message Ingestion
If real-time sources are part of the data platform, the architecture must include mechanisms to capture and store real-time messages for stream processing. Delayed processing and loss of data are some common concerns that companies experience when capturing and storing diverse and dynamic real-time data streams.
Solution 4: Utilizing either simple data stores or more sophisticated message ingestion stores.
Such data stores can include Azure Event Hubs, Azure IoT Hubs, or Apache Kafka. In AWS, users can consider AWS Kinesis, while Google Cloud users can opt for Google Cloud Pub/Sub for real-time data ingestion.
Requirement 5: Timely Capturing of Insights from Stream Processing
After capturing real-time messages, a typical big data solution processes them through filtering, aggregation, and other operations before writing the results to an output sink. Timely extraction of meaningful insights from the vast influx of real-time data stream can be a challenge.
Solution 5: Implementing advanced stream processing technologies.
Azure Stream Analytics or open-source Apache streaming technologies such as Spark Streaming in an HDInsight cluster can be an option. Kafka Streams, the stream processing library provided by Apache Kafka, also allows for real-time processing of data within the Kafka ecosystem. Confluent’s Kafka Platform extends Kafka’s capabilities by offering additional tools and functionalities, making enterprise implementations easier.
Requirement 6: Real-time Access in Analytical Data Stores
Processed data is often served in a structured format for analysis. When a data warehousing solution struggles to provide real-time access to data, it limits the speed and agility of analytical processes.
Solution 6: Rearchitecting the analytical data store by leveraging modern cloud-based data warehousing platforms.
Organizations can consider adopting modern cloud-based data warehousing platforms such as Snowflake, Databricks, or Azure Synapse Analytics. Azure Synapse Analytics offers managed service for large-scale, cloud-based data warehousing. Snowflake’s cloud-based data warehousing platform provides the much-needed flexibility and scalability for analytical workloads. Databricks offers a collaborative platform to seamlessly integrate data science, engineering, and analytics. Additionally, Amazon Redshift, which is a fully managed data warehouse service in AWS, is also optimized for high-performance analysis and can be an option to address the challenge of optimizing the performance and scalability of the analytical data store.
Requirement 7: Support for Advanced Analysis and Reporting
Organizations build a data platform with the ultimate objective of deriving insights from the data through analysis and reporting. When an organization’s analytics platform is unable to efficiently handle diverse analytical workloads, provide interactive data exploration, or support self-service business intelligence (BI) for actionable insights, it’s time to revisit the data platform architecture.
Solution 7: Transitioning to modern analytics platforms.
These modern analytics platforms such as Databricks, Looker, or Tableau can enhance capabilities for interactive data analysis, visualization, and self-service BI. The new architecture may involve data modeling layers such as multidimensional OLAP cubes or tabular data models, and support for data exploration using analytical notebooks such as Jupyter, Microsoft R Server, or other languages like Python or R.
Requirement 8: Effective Orchestration in Data Processing Workflows
Automated coordination and management of various data processing tasks such as data transformation, movement, loading, and report generation within a workflow is critical for modern businesses. It ensures the tasks are executed in the correct order and dependencies are managed effectively.
Solution 8: Rearchitecting the orchestration layer.
When existing orchestration technologies struggle to efficiently manage complex workflow, rearchitecting the orchestration layer can enhance workflow automation and provide the agility needed to accommodate changing data processing demands.
Orchestration technologies such as Azure Data Factory or Apache Oozie and Sqoop are utilized to automate workflows. Adopting modern and scalable solutions such as Apache Airflow, Kubernetes-based orchestration, or cloud-native workflow services like AWS Step Functions can also be considered.
Some Interesting Use Cases
Here are some real-world examples that showcase how organizations from different sectors – manufacturing, banking and financial services, and healthcare – can leverage the dynamic capabilities of big data architectures and derive actionable insights for innovation.
A Healthcare Provider Achieved New Data Efficiencies and Optimized Patient Care by Rearchitecting their Cloud-based Data Platform
Challenge: A leading provider of in-home renal care services, despite leveraging Azure Cloud for its data platform, experienced numerous challenges due to the limitations in its architecture. Their data platform was hindered by data silos, manual processes of data integration, and frequent downtimes– all of which impacted timely access to data for critical insights.
Solution: The company redesigned the architecture of its data platform, integrating it with tools like Apache Spark to speed up data processing and gain real-time insights. Robust data pipelines were built to automate ingestion. The canonical data model (CDM) allowed them to centralize and standardize data for consistency and accurate output.
Value: The new architecture of the data platform allowed the healthcare provider to overcome the limitations in leveraging the full potential of data for optimizing patient care, streamlining operations, and making data-driven decisions.
A Banking & Financial Services Organization Automates Its Data Pipelines by Implementing a Serverless Data Platform
Challenge: A banking and financial services (B&FS) organization migrated its operations to a cloud-based data platform. However, it still faced significant challenges in managing the surging volumes of customer transactions, financial data, and regulatory reporting requirements. They experienced scalability issues, operational complexities, and high maintenance overheads – all rooted in their data platform’s architecture.
Solution: The company decided to rearchitect its existing data platform and infrastructure, making a shift to a serverless data platform within its existing cloud environment. Serverless technologies enabled them to automate data pipelines, ensuring efficient and timely movement of data through various stages, from transactional data processing to regulatory reporting. In this case, the B&FS company used AWS Lambda for serverless compute, AWS Glue for serverless extract, transform, and load (ETL) processes, and Amazon S3 as a serverless storage solution.
Value: Rearchitecting the data platform improved the company’s adaptability to evolving customer expectations, cybersecurity threats, and regulatory requirements.
A Manufacturing Company Enhances Its Data Ingestion Layer for Predictive Analytics
Challenge: A global automobile manufacturer faced data challenges arising from the large volumes of performance data generated by the IoT sensors on its multitude of machinery. The existing data ingestion layer struggled to efficiently manage this data, leading to delays in identifying anomalous machine functions and implementing preventive measures.
Solution: To address this challenge, the organization decided to rearchitect its data ingestion layer. They transitioned from a traditional ETL (Extract, Transform, Load) approach to a more agile and scalable data ingestion layer. A unified data pipeline that could seamlessly handle different data formats in real-time was created using Apache Kafka as the central streaming platform. For scalability, the organization seamlessly integrated Kubernetes, an open-source container orchestration platform.
Value: The rearchitected data ingestion significantly improved the organization’s efficiency in managing IoT data. As a result, they successfully optimized their machinery’s performance and prevented production disruptions.
A Healthcare Technology Company Modernizes Its Legacy EHR Solution for Better User Experience
Challenge: A prominent healthcare technology company had its EHR solution for the behavioral healthcare sector built on legacy software and hosted on-premises. This legacy system lacked flexibility and became redundant, impacting the efficiency of the users of the solution.
Solution: The company embarked on a strategic rearchitecting initiative, transitioning from the outdated on-premises model to a modern, cloud-based architecture using Azure Cloud services. The new solution incorporated a technology stack featuring ReactJS for enhanced user interface, .NET for application logic, SQL for data management, and Azure Cloud for scalable and secure hosting.
Value: The rearchitected EHR solution offered behavioral healthcare providers a more agile and responsive platform that could be easily scaled to handle varying workloads. ReactJS enhanced the user experience, making the interface more intuitive for users.
Taking the Right Steps
Looking to create a modern and flexible data platform architecture that supports advanced analytics and AI? A meticulous and forward-looking approach to rearchitecting is the key.
At KANINI, we assess the effectiveness of the previous modernization efforts to identify bottlenecks, enable the adoption or enhancement of cloud-native features if not already in place, and leverage the latest solutions such as serverless computing, managed services, and cloud-based storage for optimum scalability, flexibility, and cost-effectiveness.
Speak to our experts to evaluate your existing data capabilities and build a roadmap for long-term success in your data journey.
Author
Deepika Jayakodi
Deepika Jayakodi is a Data Architect at KANINI, bringing on board her decade-long expertise in Data Analytics, Warehousing, Business Intelligence, and Solutioning. She is an expert in project management, particularly in the US Healthcare, BFSI, and Manufacturing sectors. Deepika’s passion lies in architecting cloud data pipelines to deliver intelligent end-to-end solutions, demonstrating strategic implementation and analytical prowess.
