Agriculture Industry │Systems Integration │UI/UX
We distribute large data sets across clusters of computers using Apache Hadoop’s framework and simple programming models. Gain better big data insights and improved flexibility, scalability, and cost-effectiveness with our Hadoop development services.
Chetu's Hadoop development 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 development 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.
Our expert developers understand dynamic market trends, as well as how to create a smooth transition of your existing platforms and frameworks using Hadoop migration.
Our team of experienced software developers provides best-in-class Hadoop development and implementation services for big data solutions.
Optimize your existing Hadoop platform for better results. Our Hadoop experts will customize and optimize your platform to add relevant 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 analytics modules to help you make important decisions based on accurate, real-time information.
Our talented and experienced Hadoop experts help enterprises to strategize, build, implement, integrate and test custom Hadoop solutions.
We offer comprehensive data setups and data pipeline streamlining from storage to data analysis, allowing you to effectively manage your data operations and analytics.
We use next-generation big data technologies, such as Apache Spark, Apache Hive, Apache Cassandra, and more to provide you with the most effective and high-performance 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, using DataNode and NameNode architectures to distribute file systems for data access throughout custom Hadoop clusters.
ELT data
Archiving
Big Data analytics
Pattern matching
Batch aggregation
Data warehousing
Cost-effective data
Data transformation
We provide a business toolkit for video service providers to improve customer engagement, marketing performance, content personalization, retention, and more to ramp up your 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 engineer Hadoop solutions for seamless processing and large-scale data storage, utilizing:
We use Apache Drill to provide data-filled distributed applications of large-scale datasets.
We use Apache Zookeeper to provide a hierarchical key-value store.
We use Apache Hive to create SQL-like interfaces to query data stored in file systems and various databases.
We use Apache HBase to provide bigtable capabilities for Hadoop, running on top of HDFS and Alluxio.
We use Hadoop MapReduce to effectively distribute large data set processing and compute commodity hardware clusters.
We use Apache Spark to a programming interface for fault tolerance and data parallelism clusters.
Our developers use YARN to expand Hadoop by allowing it to process and run data for batch processing, interactive processing and stream processing.
We employ Apache Mahout to create scalable machine learning algorithms.
We use Hadoop HDFS’s DataNode and NameNode architectures for optimized data storage and Hadoop cluster access.
We use Apache Solr to provide text search, dynamic clustering faceted search, database integration, indexing, document handling, hit highlighting, and NoSQL functionality.
We use Apache Pig to engineer high-performance programs that run seamlessly 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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