Converting ETL processes with big data frameworks
As the volume and complexity of data continue to grow, traditional ETL processes are struggling to keep pace. Big data frameworks, such as Apache Hadoop, Apache Spark, and AWS, offer a powerful solution for migrating and managing big data workloads, enabling organizations to process, analyze, and extract valuable insights effectively from their vast data repositories.
Getting ready
Let’s discover how AWS can help you overcome the limitations of traditional ETL processes and unlock new possibilities for data analysis:
- Challenges of traditional ETL in big data: Traditional ETL processes face several limitations in handling the massive scale and complexity of big data:
- Scalability: Traditional ETL tools aren’t designed to handle the massive scale of big data, leading to performance bottlenecks and slow processing times
- Flexibility: Traditional ETL processes are often rigid and inflexible, making it difficult to...
