Software Integration Maximizes Interoperability for Agritech Provider
Agriculture Industry │Systems Integration │UI/UX
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(954) 342 5676 
+44 137 243 2466
Let's Talk! (954) 342 5676 +44 137 243 2466
We use Apache Hadoop's framework for the purpose of distributing large data sets across clusters of computers using simple programming models. Gain better insights from big data and much-needed scalability, flexibility and cost-effectiveness.
Chetu's Hadoop programming boasts developers with experience in related technologies such as Spark, Scala, Python, Cloudera, Hive and Impala that enable the massive storing of data and running applications.
Our MapReduce framework implementation enables a significant volume of data on large clusters to be processed, as well as generates big data sets with a parallel, distributed algorithm.
Our Hadoop developers perform integration solutions with software components such as Hive, Pig, Flume, Ambari, HCatalog, Solr, Cassandra, Sqoop, Zookeeper, HBase, and Oozie.
Our Hadoop solutions enable enterprises to gain better insights from data and achieve scalability, flexibility and cost-effectiveness.
Our developers utilize the Hadoop YARN (Yet Another Resource Negotiator) architecture that enables system resource allocation to applications operating in clusters while organizing tasks.
Our developers provide maintenance services for your critical business processes. Our developers provide improved functionality and lessen the need for continued maintenance.
We optimize your organizations performance with custom Hadoop development. We help IT departments balance current workloads with future storage and processing needs.
At Chetu we understand dynamic market trends and how they affect your business goals, so, we ensure a smooth transition of your existing platforms and frameworks to a more stable Hadoop solution.
Whether you currently utilize Hadoop clusters or you want to implement seamless Apache Hadoop services from scratch, our team of Hadoop developers are ready to help you.
Optimize your existing Hadoop platform for better results. Our Hadoop experts work on your platform to customize and make relevant to the current trends and business requirements.
We provide SAS/ACCESS to Hadoop with features such as metadata optimization and integration, query language support, Hive interface support, SAS statement mapping, and seamless data access.
We integrate real-time insights with multiple solutions and existing Hadoop clusters for real-time responsive analytics.
Our talented and experienced Hadoop experts help enterprises to strategize, build, implement, integrate and test custom Hadoop solutions.
We provide you with a comprehensive data setup that manages your data operations and analytics. We set up a streamline data pipelines from storage to data analysis.
We make use of next-gen big data technologies like Apache Hive, Apache Cassandra, and Apache Spark to provide our customers with the most effective big data solutions.
Chetu is able to provide Big Data consulting and development services, helping companies bridge the gap between the overflow volume of complex data and the ability to perform in-depth analysis to interpret and report.
We provide custom HDFS (Hadoop Distributed File System) services that successfully use NameNode and DataNode architecture to utilize distributed file systems for access to data throughout custom Hadoop clusters.
ELT data
Archiving
Big Data analytics
Pattern matching
Batch aggregation
Data warehousing
Cost-effective data
Data transformation
JUMP Data-Driven Video provides a business toolkit for video service providers to increase retention, customer engagement, content personalization, and marketing performance to ramp up businesses' ROI. JUMP's platform accumulates video service providers' backend and frontend data sources that are enriched through big data, artificial intelligence, and machine learning capabilities.
We develop solutions for large-scale data storage and seamless processing utilizing:
We provide data filled distributed applications for interactive analysis of large-scale datasets by using Apache Drill.
Our developers provide a hierarchical key-value store using Apache ZooKeeper.
We create SQL-like interface to query data stored in various databases and file systems using Apache Hive.
Our team uses Hbase to runs on top of Alluxio and HDFS and provide bigtable capabilities for Hadoop.
We utilize Hadoop MapReduce’s software framework to distribute the processing of large data sets to compute clusters of commodity hardware.
Our developers use Apache Spark to provide an interface for programming entire clusters with fault tolerance and data parallelism.
Our developers use YARN to expand Hadoop by allowing it to process and run data for batch processing, interactive processing and stream processing.
Our team employs Apache Mahout to create implementations of distributed and scalable machine learning algorithms focused primarily on linear algebra.
We utilize Hadoop HDFS’s NameNode and DataNode architecture to provide primary data storage and access to Hadoop Clusters.
Chetu’s developers use Solr to provide document handling, text search, hit highlighting, dynamic clustering faceted search, indexing, database integration, and NoSQL functionality.
We use Apache to create high performance programs that run on Apache Hadoop. Our developers use Pig to execute its Hadoop jobs in Apache Spark and MapReduce.
Cheth’s developers utilize Apachee Oozie as a workflow scheduler system to manage Apache Hadoop jobs.
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Headquarters & Delivery Center
10167 W. Sunrise Blvd.
Suite 200
Plantation, FL 33322
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