Overwhelmed by Operational Challenges in Scaling the ELK Stack?

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ELK Stack

Operational Nightmare in Maintaining and Scaling

Complex set-up, time consuming maintenance, burdensome troubleshooting across multiple tools.

Operational challenges in tuning Elasticsearch clusters to handle large amounts of data.

Elasticsearch customers are responsible for managing infrastructure, data lifecycle, and data tiers, leading to an unnecessary burden on valuable engineering resources.

Architecture

Scalability

Data Tiers

Unified Observability

Data Encryption

Premium Support & Customer Success

Cloud-native. Separates compute from storage

Elastically scales as needed

All data is always kept hot

All telemetry data in one data lake with one query language and one consistent UI for faster correlations and troubleshooting

Included by default. All data is encrypted in-transit and q’at-rest

Included

ELK Stack

Traditional. Monolithic. Tightly coupled compute and storage

Manual. Requires hardware provisioning, tuning Elasticsearch clusters.

Keeping all data hot is cost prohibitive. Users need to create, maintain, and migrate between data tiers – hot or archives and manually rehydrade and reindex data

Multiple tools and components to manage.

Manual processes of setting encryption can be complex and error-prone.

Available at an additional cost from Elastic

Understanding the Total Cost of Ownership (TCO) of the ELK Stack

The ELK Stack—comprising Elasticsearch, Logstash, and Kibana—is a popular solution for log management and data analytics. However, as organizations scale, the complexities and costs of maintaining ELK also grow. Read our solution brief to learn the key reasons to replace the ELK stack, including both business and engineering perspectives.

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