Big Data Consulting Services

Indium can advise on various aspects of Big Data like Infrastructure, Architecture, Implementation and Management.

Big Data Infrastructure

Infrastructure Consulting

Strategy and Assessment – Process based approach to assess current infrastructure, evaluate further requirements, recommend optimal solutions and design an implementation roadmap.

Hardware Selection and Implementation – Helping customers calculate requirements, understanding configuration options, liaising with vendors and partners for procurement and implementation of hardware solutions to meet current and future needs.

System Infrastructure Setup & Management – Setting up the big data environment and enabling high performance business analytics based on industry best practices.

  • Setting up Hadoop clusters in Data Centers using various distributions like Cloudera, Hortonworks, MapR & Apache
  • Monitoring clusters and jobs. Trouble shooting, adding / removing nodes
  • Setting up cluster on AWS EC2 / AWS EMR, Azure HD Insights and monitoring

Capacity planning – Plan and provision for future scalability requirements to meet expected performance and fault-tolerance parameters.

  • Indium can help in capacity planning of big data platforms
  • Estimate optimal customer size based on data sources, data volume and complexity of business use cases

Big Data Security

  • Indium implements Hadoop Security using Kerberos, Ranger, Sentry
  • Kerberos for Hadoop users, Ranger and Sentry for other ecosystem components like Hive, Hbase, HDFS etc

Solutions that work, BIG TIME!

Cloud Computing

Implementing on Cloud

Cloud Migration – Efficient and secure migration of client data and applications to the target cloud environment

Dashboard Enablement – Support with meaningful dashboards that provide key information such as status, help in proactive monitoring and reveal root cause in case of issues.

Security Enablement – Identity protection, access management and network controls ensuring implementation of security standards on the cloud

Network Enablement – Ensuring performance, access, bandwidth utilization, response time, firewalls and so on

Desktop Virtualization – Ensuring a highly secure and flexible desktop delivery model

We Analyze, You Grow!

Big Data Integration Solutions

The need for shorter time to market, a reduction in total cost of ownership (TCO), and to manage or replace obsolete and expensive data-integration patterns currently deployed are compelling corporates to opt for data integration services where a group of services runs on service-oriented architecture with low latency time periods.

Due to the complexity involved in connecting structured and unstructured data, existing solutions may leave pockets of data that are buried deep unexplored. As the volume and demands increase, migrating everything to cloud data lake architecture may pose a greater challenge. A managed Hadoop or big data solution can help meet business objectives at lower CapEx and OpEx.

Indium Software Data Integration services are aimed at helping organizations move to a completely managed data architecture.

Some of the benefits it imparts includes:

Lowering TCO and operational costs
Improved data governance
SLA and manageability, leading to higher ROI

Indium Software’s process oriented approach simplifies the use of Apache Hadoop, MapReduce, Hive, HDFS with Big Data Integration Solutions.

Indium Software also offers a platform-agnostic data integration solution that integrates data irrespective of the following:

Required frequency
Communication protocol
Business rules to determine the integration patterns

Indium Software works with the following technologies to achieve the objectives:

Extract Transformation and Load (ETL)
Enterprise Application Integration Products (EAI)
Enterprise Data Replication (EDR)

Indium Software’s expertise combined with the tools ensures data integration from a variety of endpoints such as data warehouse, big data, APIs, applications, and more.

Big Data Optimization

  • Indium Software supports Optimizing Hadoop Ecosystem components like Hive, Hbase, Spark, Kafka, Solr, Elastic Search, Apache Kylin for read performance / write performance
  • GC cleaning and optimizing Map Reduce jobs, JVM tuning, setting up performance parameter in component level and system level as well as basing it on business use cases, volume and velocity

Big Data Testing

Big Data is not a buzzword anymore. Most organizations have started adopting to Big Data and it has become an integral part of the organization’s decision-making process. With huge quantities of data at their disposal, mostly unstructured data, organizations are struggling to get the best out the data in hand. Data comes in all form, with volume and velocity, and the data is updated at a rapid pace hence the processing time should be quicker too.

Indium Software’s Big Data Testing services guarantee complete validation of both structured and unstructured data thus helping achieve superior data quality.

Big Data Agile Delivery Framework

Agile Managed Services

Work closely with business stakeholders and understand key business requirements. Demonstrate business value through Big Data use cases(improve customer experience, optimize operations, reduce churn, manage risks etc.) that aligns with the business requirements. Devise a strategic roadmap, evaluate and recommend technology choices, after analyzing existing infrastructure and performing readiness assessment.

Conceptualize the Big Data Solution through solution architecture augmented with technologies and infrastructure that aligns with the business strategy.

Based on the quantum of enhancement/ business alignment required, only implementation phase or both Pilot/Prototype and Implementation phases are iterated in every sprint

Deliver Proof of Concept (PoC) and design and develop an end-to-end pilot/prototype application that demonstrates business value, technology capability, and gives a fair idea about the time-to-value. Test the pilot/prototype and then obtain sign-off from business.

Develop the low-level design and database design, and then extend the pilot/prototype application developed by writing additional program as required. The existing systems would then be integrated and data would be migrated, before deployment and testing. Sign off from QA team is obtained.

Cut over activities would be performed and production environment would be setup in parallel. The QA signed off application would then be moved to production. The application is maintained and administered for the warranty/maintenance period duration.

  • Early risk reduction approach
  • Iterative and Incremental delivery of valuable chunks within weeks
  • Closer alignment with business needs
  • More clarity and control over requirements

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