MapR Professional Services brings world-class expertise to help you get the most out of your Hadoop investment. From implementation to data migration to tuning and optimization to data engineering and advanced analytics, the MapR Professional Services team will work with you every step of the way. We offer a wide range of services to help you plan, implement, deploy and manage your big data installation.

As the technology leader in Hadoop, MapR employs the best and brightest engineers and data scientists in the big data industry. MapR augments its in-house expertise by collaborating with industry experts and certified service providers to ensure you get the best Hadoop experience.

Core Hadoop Services

  • Implementation

    We will assess your current environment, and then install and configure the MapR Converged Data Platform cluster for optimal service levels for your particular infrastructure.

  • Quick Start Data Migration Service

    If you're currently using a different Hadoop distribution, we'll assess your current environment, move the use cases and data over to your new MapR cluster, and ensure a smooth transition to a true production Hadoop environment with zero downtime and zero data loss. The migration service maintains operation of the existing distribution while transitioning to MapR.

  • Upgrade Services

    Take advantage of the latest innovations in the MapR Converged Data Platform. These include updates from MapR at the platform services level, but also at the community ecosystem level. We can help you successfully get to the latest releases for your critical big data deployment.
    With the MapR 5.1 Step-Up Program, we can help you get to the latest release of MapR. Click here for more information, including why you should upgrade to MapR 5.1.

  • Solution Design/Implementation

    Our Data Engineers will help you fully realize the benefits of your MapR investment by designing and implementing a comprehensive solution that will perform optimally within your particular environment. All aspects of your implementation will be addressed, including hardware infrastructure, data sources, ecosystem software, and operations considerations.

  • ETL

    The first step in any Hadoop implementation is data ingestion and transformation. MapR will develop and implement a customized ingestion/ETL plan that includes identifying the multiple data sources and file formats, transforming them to meet your needs, and loading the data into the data structures best suited to your needs for further analysis.

  • Application Development

    Our team of data engineering experts can help you design, build and optimize your big data applications. Whether it’s building Java MapReduce jobs, implementing in Apache Pig, building distributed indexes, constructing Apache Hive (HQL) queries, or building complex data models, MapR provides the experience and expertise you need.

  • Data Aggregation

    Our Data Engineers will assist you with data aggregation so that you can quickly and accurately consolidate and fuse data from various sources.

  • Presentation Layer

    From start to finish, MapR will create the most optimal data flow: from integrating data sources, to Hadoop, to the presentation layer.

  • Use Case Discovery

    As part of our Advanced Analytical services, MapR will send a Data Scientist to your site who will conduct a use case discovery session in order to gain a clear understanding of business priorities as well as existing workflows/data sources available for analytics. From there, use cases will be identified, and a road map will be created for big data development and training so that you can become self-sufficient.

  • Data Modeling

    Our Data Scientists will identify the best way to transform and structure raw data, and will create a custom data model for your particular use case.

  • Statistical Methods

    MapR will create the right algorithm or statistical model in order to meet your speed, reliability and maintenance goals.

  • Machine Learning

    MapR will assess your business needs, define the business case, and recommend the appropriate machine learning methods.

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