ScienceSoft Healthcare
ScienceSoft Finance
High-Availability Hadoop Cluster Implementation to Enhance Data Reliability at ANI Networks
Summary
ScienceSoft implemented a high-availability Hadoop cluster for a US nationwide telecom provider, eliminating the single point of failure in a business-critical data platform and greatly reducing downtime risks.
About ANI Networks
ANI Network is a US telecommunications provider delivering wholesale, carrier-class voice and data solutions. Since 1989, ANI has served over 400 clients, including Fortune 500 enterprises, rural carriers, wholesale providers, and mobile operators. Known for its scalable network, ANI Networks is committed to delivering reliable, uninterrupted service to its partners nationwide.
ANI Networks’ business-critical data platform used a Hadoop cluster with a single NameNode, which became a single point of failure. If the NameNode went down, the entire cluster would become unavailable, creating a high risk of downtime and potential loss of critical data. ANI Networks approached ScienceSoft to switch its Hadoop cluster to the High Availability mode and update any related components to prevent potential operational conflicts or other downstream issues. The project’s key priority was ensuring continuous cluster operation and minimizing the risk of data loss or service interruption in case of hardware failures.
Solution
After studying ANI Networks’ data processing environment to avoid potential failures or data loss, ScienceSoft deployed and configured the Hadoop cluster in High Availability mode. The work included:
- Introducing an additional standby NameNode to eliminate the single point of failure.
- Configuring HDFS High Availability and updating all components that interact with NameNode services (including Hadoop, TEZ, Hive, Hive Metastore, and HiveServer2) to ensure correct operation in the High Availability setup.
- Implementing monitoring and alerting capabilities to detect performance deterioration early and ensure system transparency for future maintenance.
From a user perspective, the workflow remained unchanged: applications continue processing data as usual, but the system can now automatically switch to the standby NameNode if the main one fails, helping minimize service disruptions.
Joseph Jackson, Director, IP Network Engineer at ANI Networks, says:
Our cooperation with ScienceSoft was productive and transparent. The team quickly understood how important it is for us to maintain the reliability of our data processing ecosystem at the highest possible level and enhanced its architecture so that it is even more resilient and stable than before. They also set up monitoring and alerting that give us clear visibility into cluster health and support proactive management. We plan to continue working with ScienceSoft’s team as we evolve our data workflows and support the growth of our services for clients.
Key Outcomes for ANI Networks
With the new High-Availability configuration, ANI Networks received a fault-tolerant Hadoop cluster that continues to operate even if the primary NameNode’s hardware fails. With the solution, ANI Networks achieved the following:
- Eliminated the NameNode as a single point of failure in the Hadoop cluster without affecting internal user experience or end client operations.
- Minimized the risk of service downtime and data unavailability.
- Enabled real-time visibility into cluster health through monitoring and alerting.
Satisfied with ScienceSoft’s assistance, ANI Networks plans to involve the team to further optimize its big data pipelines for quicker query processing.
Technologies and Tools
Hadoop, MapReduce, Tez, Hive, Hive Metastore, HiveServer2, HDFS High Availability (HDFS HA), Python, Shell.
