According to the IDC, the amount of data created in the next three years is estimated to be greater than that generated in the past thirty. With data explosion, comes the need to efficiently manage, adapt, and scale the growing volume. Many customers opt for ELK for its cost saving, scalability, and open source benefits – however, it is important to understand the pitfalls of maintaining legacy tools and consider the advantages of other solutions.
We recently hosted a webinar diving into the challenges with Elasticsearch and how DataSet differentiates itself in log management with Dave Gold, Field CTO SentinelOne , and Anthony Johnson, Field CTO of DataSet. If you missed the webinar, watch the on-demand recording here.
Here are some key takeaways:
Main Challenges with Legacy and On-Premise Tools
- Cannot support the scale and speed requirements of modern architectures such as Kubernetes and microservices
- Relies heavily on keyword indexing, batch, and query
- Autoscaling data is hard – compute and storage are tightly coupled
- Open source doesn’t necessarily mean free, consider operational costs from the beginning
ELK Hidden Costs
There are hidden costs when it comes to maintaining hardware. This can lead ELK deployments to be more expensive and time-consuming than suggested:
- Sharding is required which divides indexes across nodes. If there are too many on a single node, this can lead to increased latency, storage usage, and make it more difficult to scale.
- While it is free and open to modifications, ELK requires heavy lifting in the form of additional paid services, support, and features. This increases operational overhead requiring developers to manage, maintain, and deploy updates.
- Managing the infrastructure for ELK requires additional setup work in making sure the data is backed up and protected with the right type of hardware that can scale large volumes of data.
Comparing Scale and Query Data
Using Logstash generators, watch how DataSet stacks up against ELK when handling one, five, and even up to forty generators. You can see the whole demo in the webinar linked here. See how much more data (TB) is faster and easily ingested over different retention periods compared to that of ELK. It is clear that DataSet differentiates in:
- Achieving faster ingestion and query speed, taking seconds at a petabyte scale
- Scaling efficiently and autoscaling without the need to rebalance nodes, manage storage, or allocate resources.
- Reducing operational overhead and total cost of ownership
The DataSet Difference:
- Live Data - Schema-less ingestion and index-free architecture means that data shows up in real time, scaling to petabytes of data. No need to worry about indexing and sharding.
- The Power of a SaaS Cloud Platform - Store all log and event data in one place accessible to different teams.
- Separate Storage from Compute - Choose between DataSet S3 or Bring Your Own S3 bucket. Our columnar format allows for fast searches and low cost storage.
- Streaming Engine -Drive live dashboards and alerts to offload queries to answer in real time.
- Best in class security - A security first architecture helps meet encryption and compliance standards.
Lower Your Total Cost of Ownership:
DataSet can lower Total Cost of Ownership by 60-80% over 3 years compared to traditional tools. With the platform experience:
- Paid full managed cloud system with no maintenance, tuning, and scaling that is cheaper than open source
- Faster, scalable, lower operational expense
- Increase productivity with your teams spending 10x less time managing and operating the platform
- Integrations that help pull data in (Logstash, Kafka, fluentD)
- White glove support
Read more about the benefits of replacing ELK in our latest whitepaper, The Business and Engineering Case to Replace ELK Stack