Big data analytics involves processing and analysing vast amounts of data to extract valuable insights and knowledge. It leverages advanced technologies such as cloud computing, machine learning and data visualisation, to process and analyse big data in real-time. Big data analytics has the potential to transform industries and improve decision making, but it also poses technical and organisational challenges such as data privacy and security.
One of the key challenges in big data analytics is the amount of computational power and time required to process and analyse vast amounts of data. This can result in high costs, both in terms of hardware and personnel, and increased processing time, which can delay decision making and result in lost opportunities. For example, processing terabytes of data in real-time requires substantial computing resources, such as powerful servers and high-performance storage systems. In addition, the process of cleaning and transforming the data, which is necessary for accurate analysis, can be time-consuming and requires specialised skills, leading to higher labour costs. These factors all contribute to the overall cost of implementing big data analytics solutions and can make it challenging for organisations to fully leverage the potential benefits of big data.
To address the challenges of big data analytics, such as high costs and slow processing times, Cevo offers a solution to implement Apache Spark on Amazon Web Services (AWS) Elastic Kubernetes Service (EKS). The goal is to boost efficiency with Spark and Kubernetes.
We offer following extensions on this big data platform ecosystem:
- Integration of Apache Airflow to orchestrate the data engineering pipeline and act as a client to invoke Apache Spark jobs on the modern platform, which auto scales on demand.
- Integration with S3 as a data lake to store and access data, and ensure privacy
- Implementation of Spark History Server to monitor Spark jobs
- Implementation of metrics monitoring and alerting using Prometheus and Grafana
- Provision of a data science working platform using Jupyter notebook within the same package
- Integration with Redshift, a data warehouse service in AWS, to write the structured results from the Spark job execution
- Integration with data lineage using Apache Spline
- Provision to use Apache Iceberg tables using Spark for Data Lakehouse platform.
- Development of data science and ML models using Conda pack integrated for docker images, which can be executed on this platform
- Option for multi-tenancy if you are running the ecosystem as a shared platform for all organisational data analytics needs
Putting this solution into practice, we were engaged by a customer to replace their existing Cloudera solution with Apache Spark on AWS EKS to overcome challenges relating to their big data analytics needs. This implementation resulted in a 300% increase in performance while reducing costs, the removal of paying licensing costs and simplifying deployment and management.
The performance improvement that you can expect from migrating from Cloudera to Apache Spark on Kubernetes will depend on several factors, including the size and complexity of your data, the performance characteristics of your current infrastructure, and the specific workloads you are running.
In general, Spark provides better performance than Cloudera due to its in-memory data processing and ability to cache intermediate data. With Spark on Kubernetes, you can take advantage of the scalability and performance benefits of both technologies.
- Scalability: Apache Spark can scale to handle larger amounts of data than Cloudera and provides a more efficient solution for processing big data. As data volumes continue to grow, it is becoming increasingly difficult to manage and process large amounts of data using traditional data platforms. Modern data platforms, such as Apache Spark, are designed to handle large-scale data processing and analytics tasks, and can easily scale to meet the needs of the enterprise.
- Performance: Spark is faster than Cloudera due to its in-memory data processing and ability to cache intermediate data. Apache Spark is designed to take advantage of distributed computing and in-memory processing, which can significantly improve the performance of data processing and analytics tasks.
- Flexibility: Spark supports a wide range of data sources and can be integrated with various tools and technologies.
- Ease of use: Spark provides a simple and user-friendly API for programming and running big data applications.
- Cost-effective: Spark provides a more cost-effective solution for big data analytics than Cloudera, as it requires less hardware and infrastructure. Traditional data platforms, such as Cloudera Data Hub, can be costly to maintain and operate. Modern data platforms such as Apache Spark, can be more cost-effective, as they are open-source and can be run on commodity hardware.
- Kubernetes integration: Apache Spark can be deployed and run on Kubernetes, providing a more flexible and scalable solution for big data analytics.
- Data governance: With increasing amounts of data, it’s important to have a proper data governance framework in place. Apache Spark provides features such as data lineage, data discovery, and data cataloging to facilitate better data governance.
- Security: As the amount of data increases, so does the need for security. Apache Spark provides advanced security features such as authentication, authorisation, and encryption to secure your data and systems.
In conclusion, implementing Apache Spark on AWS EKS provides a solution to the challenges of big data analytics, by improving performance, reducing costs and simplifying deployment and management. By leveraging the cloud and open-source technologies, organisations can achieve better outcomes from their big data analytics initiatives.