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7 Essential Practices for Cloudera Optimization

Alon Berger

Product Marketing Manager, Intel Granulate

Cloudera is a hybrid data management solution that can be especially useful for data management, data processing, analytics and Machine Learning applications. Enterprises have benefited from the technology’s ability to handle data securely at scale.

However, with the advantages that the Cloudera Data Platform brings, there also exist challenges for intelligent companies that desire an optimized infrastructure. Data engineers might have trouble with capacity management on CDP, leading to more manual work and additional licensing costs, which can add up quickly. Most solutions are only recommendation-based or don’t meet the scrutiny of enterprise security standards, so it’s hard to depend on 3rd party optimization solutions. These hurdles can stand in the way of advanced performance, reduced costs or improved throughput. 

When embarking on a cost optimization journey for applications running on Cloudera, the following strategies can impact their effectiveness and efficiency. Here’s an ordered list:

1 – Monitor Resource Utilization

Continuous monitoring of resource utilization is vital for identifying inefficiencies and understanding usage patterns. By tracking metrics such as CPU, memory, and storage usage, you can gain insights into when and where your resources are being strained or underused. 

This data serves as the basis for making informed decisions about scaling, optimization, and cost control. Regular monitoring also helps in predicting future needs, ensuring that you’re always one step ahead in optimizing your environment.

Cloudera has recently released an observability tool to help enterprises manage costs and says that early adopters have already realized some significant savings with its observability tool.

2 – Choose the Right Instance Types 

Selecting the most appropriate vertically scaling instance types is crucial as it ensures that you have the necessary resources to meet your performance needs without overspending. This involves understanding the characteristics of your workload and matching them with the instance types that Cloudera supports, considering factors like CPU, memory, and I/O capabilities. 

Get the Big Data Optimization Guide

Making an informed choice at the outset can prevent costly reconfigurations down the line and avoid underutilization or over-provisioning. It’s a foundational step that sets the stage for effective cost optimization throughout the lifecycle of your applications.

3 – Implement Cluster Auto-Scaling

Dynamic cluster scaling is a powerful way to align your resources with actual demand, ensuring that you’re not paying for idle capacity in your Virtual Warehouse. By automatically scaling your cluster nodes in or out based on workload, you can maintain performance during peak times and reduce costs during periods of low activity. 

This approach requires a good understanding of your workload patterns to set appropriate thresholds and scaling policies. Implementing cluster auto-scaling not only optimizes costs but also enhances the agility and responsiveness of your applications.

4 – Leverage Data Compression

Data compression is a critical strategy for managing large volumes of data efficiently. By reducing the size of your data, you can significantly cut storage costs and improve I/O performance, which in turn can lead to faster query processing and reduced compute costs. Implementing compression should be an ongoing process, with regular reviews to ensure that the most effective compression techniques are being used as data types and patterns evolve. While there may be some upfront performance overhead in compressing and decompressing data, the long-term savings and performance benefits are usually well worth the investment.

To leverage data compression in Cloudera, it’s essential to choose the right compression codec (like Snappy, Gzip, or Bzip2) based on your data’s characteristics and the balance between compression rate and processing overhead. You can enable compression at various levels, including during storage in HDFS or while processing with tools like Hive and Impala.

It is also recommended to use the correct file formats for Cloudera optimization, such as opting for Parquet over less efficient formats. The choice of file format can play a crucial role in optimizing data storage and query performance.

Get the Big Data Optimization Guide

5 – Optimize Data Storage

Optimizing data storage involves more than just deleting old data; it’s about understanding your access patterns and aligning your storage strategy accordingly. Regularly archiving data that’s not frequently accessed off of more expensive storage options can lead to substantial cost savings. 

Additionally, implementing data lifecycle policies that automatically move or delete data based on age or usage can help maintain an efficient and cost-effective storage footprint. As your data grows and changes, continuous evaluation and adjustment of your storage strategy are essential to ensure you’re not spending more than necessary.

Consider using Cloudera’s Hadoop Distributed File System (HDFS) features like erasure coding and storage policies to optimize storage costs and performance according to the nature of the data and the computation needs.

6 – Use Spot Instances

Utilizing spot instances can lead to significant cost savings for certain types of workloads, particularly those that are non-critical and can tolerate interruptions. Spot instances typically offer a substantial discount compared to on-demand prices but require a strategy to handle their transient nature. 

Effective use of spot instances involves understanding your workload’s tolerance for interruption, implementing checkpointing and quick save mechanisms, and having a robust strategy for bidding and fallback. It’s also crucial to implement robust bidding strategies and failover mechanisms to seamlessly transition workloads between spot and on-demand instances as availability and prices change.

While it’s a more advanced cost optimization strategy, when used wisely, spot instances can dramatically reduce the cost of running large or variable workloads. 

7 – Autonomous, Continuous Performance Optimization

Autonomous optimization can be an effective cost reduction solution for Cloudera workloads. Intel Granulate enhances application performance and throughput without requiring any code changes. Users can find 20% capacity reduction, allowing more jobs to run concurrently and reducing the number of required licenses, thereby cutting costs. 

Get the Big Data Optimization Guide

The solution features autonomous runtime optimization and dynamic resource allocation, eliminating the need for manual monitoring and tuning while ensuring secure and reliable performance. It’s applicable across various environments, including on-prem, hybrid, multi-cloud, and lift & shift, making it ideal for sectors with high-security needs like healthcare and finance. Additionally, it supports seamless migrations to major data platforms, optimizing resources and reducing waste during transitions.

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