Developing Big Data Capabilities using Cloudera Hadoop in Telecom Sector

Big data platforms play a critical role in enabling organizations to process and analyze massive volumes of information efficiently. As data continues to grow exponentially across operational, analytical, and business domains, maintaining performance, scalability, and reliability becomes a constant challenge.

In such an environment, the Cloudera-based big data platform can serve as the backbone for large-scale data ingestion, transformation, and analytics. Leveraging key components such as Apache Hive, Spark, Kafka, and Impala, the Cloudera big data platform can support daily processing of terabytes of data while delivering real-time insights to multiple business functions.

To ensure the deployed big data platform remains stable, high-performing, and scalable, an optimization initiative needs to be designed properly and with utmost care so that it can be capable of sustaining future data growth and supporting long-term innovation.

Problem Description

Expec is engaging in a long term devops initiative with one the biggest telecommunications providers in Indonesia to deliver a robust big data platform deployment which requires continuous data availability, reliability, and performance optimization using Cloudera Hadoop technology. With multiple Cloudera services and Hadoop ecosystem tools available, it was essential to select and configure the most suitable combination to meet operational requirements.

Each technology—Hive, Spark, Kafka, and Impala—offers unique strengths but also introduces complexity in tuning, resource management, and integration. Selecting and optimizing the right mix for each workload became a challenge. Each service has trade-offs:

• Hive: reliable and batch-oriented, but slower for ad-hoc queries

• Impala: faster for analytics, but resource-intensive

• Spark: highly parallel and flexible, but requires careful tuning

• Kafka: scalable messaging layer, but needs strong governance to prevent data duplication

  
  

In addition, the absence of an integrated automation framework meant that recurring operational tasks such as ETL job scheduling, cluster monitoring, and data validation were often performed manually. This led to inconsistencies, longer processing times, and an increased risk of failure in production environments.

Proposed Solution

To address these challenges, Expec deployed a specialized data engineering team focused on designing and implementing an optimized and automated data environment. The approach combined architectural standardization, performance tuning, and workflow automation using Bash scripting.

The solution focused on three main areas:

1. Architectural Standardization – Establishing clear technology boundaries and responsibilities. Hive was prioritized for batch ETL workloads, Spark for real-time streaming and transformation, Impala for analytics, and Kafka for data integration and message transport.

   
   

2. Automation and Orchestration – Developing reusable Bash scripts to automate recurring operational tasks. These scripts handled job scheduling, data validation, log collection, and health monitoring across Hadoop services.

Implementation Overview

The implementation focused on integrating Hadoop ecosystem components into a unified and automated workflow. Bash scripting played a central role in orchestrating and maintaining this environment, ensuring stability, consistency, and reduced manual intervention.

Key implementation areas include:

• ETL and Batch Processing

  • Utilized Apache Hive as the main engine for large-scale data transformation and batch ETL processes.

  • Developed Bash scripts to manage job dependencies, monitor execution times, and trigger automated retries in case of failure.

  • Improved fault tolerance and reduced downtime during multi-hour processing workloads by integrating automated job retries, dependency validation, and recovery checkpoints within Bash-orchestrated Hive and Spark workflows.

    
    

• Streaming and Real-Time Analytics

  • Implemented Apache Spark for streaming data and near-real-time analytics.

  • Automated Spark job triggers using Bash-controlled event-based scripts.

    
    

• Low Latency Reports

  • Enabled continuous data processing and low-latency reporting with Apache Impala for sub-second query performance.

    
    

• Data Integration and Messaging

  • Leveraged Apache Kafka for high-throughput data ingestion and inter-system messaging.

  • Implemented a custom additional tools on top of the Kafka cluster deployment to enable a centralized management (e.g. topic creation and partition adjustments) and monitoring (e.g. overviewing messages on topic and monitor consumer lag) of the Kafka cluster

  • Ensured consistent data flow and reduced message delivery errors across the platform.

    
    

• Platform Optimization and Monitoring

  • Tuned Cloudera cluster resources (CPU, memory, disk) for balanced performance.

  • Automated periodic cluster health checks, temporary file cleanup, and daily performance reporting via Bash scripts.

  • Improved operational visibility and maintained optimal performance across environments.

    
    

• KPI Development and Visualization

  • Built business metrics and KPIs using HiveQL and Impala queries.

  • Integrated dashboards in Tableau and Grafana for ongoing analytics and performance tracking.

Why Expec Consulting?

• Proven Expertise – Over a decade of experience in big data architecture, Hadoop ecosystems, and enterprise automation, enabling end-to-end understanding from infrastructure to analytics.

  
  

• Hands-On Delivery – A small, embedded team of data engineers and architects directly managed platform operations, ensuring practical, results-driven implementation.

  
  

• Focus on Automation – Deep experience with Bash scripting, workflow orchestration, and system optimization helped reduce operational complexity and enhance reliability.

  
  

• Vendor-Neutral Approach – Expec selects and configures tools based on technical merit and business need, ensuring the best fit across Hive, Spark, Kafka, and Impala.

  
  

• Sustainable Knowledge Transfer – The engagement emphasized documentation, reusable script libraries, and mentoring to ensure long-term operational independence for the client team.