Leveraging data to generate meaningful and actionable insights leading to improved business decisions
Deep expertise across Hadoop & Spark
AWS Premier Big Data Consulting Partner
There is an exponential growth in the volume, variety and velocity of data. Our in-house data connectors, solution accelerators and big data integration capabilities enable faster data-driven decision making leveraging our extensive experience in data management, data warehouse implementation, real-time data integration, high volume data processing and data orchestration and reporting.
We have helped many enterprises to build their data management and analytics platforms using open source as well as cloud-based big data solutions such as Amazon Redshift, Amazon CloudSearch, Amazon Kinesis, Google BigQuery, Google Cloud Dataflow and Google Cloud Dataproc.
We have built capabilities around Big Data platform implementation from ETL, data processing, compute, data orchestration, visualization, reporting, analytics, advanced and predictive analytics, data modelling and data science. Leveraging these capabilities we offer end to end Big Data and Data Engineering services.
Data Strategy, Consulting & POC
We help businesses to determine their big data strategy and consult on improving the business performance uncovering the power of data. Our Big Data consulting includes POC/POV, technical recommendations, data source analysis, architectural consulting, capacity planning and much more.
We can help businesses with real-time data ingestion, ETL & batch processing and storage from different & complex data sources leveraging our deep expertise across big data technologies such as Hadoop (HDFS, Map Reduce, Hive, Flume, Sqoop, and Oozie) and Spark. We help businesses create real-time charts & dashboards and setup pipeline.
We use various tools such as Tableau, Chart.js, Dygraphs, D3JS and HighCharts to produce visuals and stories that generate high business impact. We generate custom dashboards, reports, alerts and metrics as per business logic and apply machine learning algorithms & data modeling to perform predictive analysis using techniques such as regression and decision trees.
End to End Data Lake Implementation
We help businesses design, architect and implement data lake frameworks and integrate data assets to derive meaningful insights without any data loss. The implementation consists of identifying data channels, data integration, backup, archive, data processing, data orchestration, and visualization along with data governance and automation.
Big Data DevOps & Managed Services
Leveraging our expertise in both DevOps and Big Data Administration, we ensure architecture setup, implementation with full automation and manage the overall performance of Hadoop clusters to ensure high throughput and availability. We also help businesses identify potential threats through, data governance and access & identity management to help ensure data security.
Big Data Testing & Automation
We ensure data quality, accuracy, consistency and completeness through big data testing and automation. Our QA engineerings verify data in a 3 stage validation including data stage validation, MapReduce Validation and output validation followed with performance testing of big data applications.
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We are proficient in Hadoop ecosystem (HDFS, Sqoop, Flume, Hive/Pig, Oozie etc.), Streaming & In Memory processing (Storm, Spark, Kafka), Enterprise Searching (Elasticsearch, Solr), NoSql Databases (MongoDB,Cassandra,Couchbase, Neo4J, Redis), Machine Learning (Mahout), Visualization (Tableau, R, D3.js, MS Excel) and Cloud Provisioning & Hosting Platforms (Amazon Web Services, Cloudera, Hortonworks). We have extensive experience in using Amazon services like Amazon EMR, Amazon Elasticsearch Service, Amazon RedShift, Amazon Kinesis etc.
Our Big data team has Cloudera Certified Hadoop developers and Administrators, Amazon certified solution architects, MongoDB Certified Developers and DataStax certified developers and trainers.
Apache Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed for near linear scaling from a single server to thousands of machines, with a very high degree of fault tolerance. It is an ecosystem of multiple components which can be chosen depending upon requirements. Hadoop is primarily a distributed computation and storage platform for batch processing. It doesn’t provide real-time insights though there are a lot of solutions which can be integrated with existing Hadoop clusters to have real-time responsiveness.
Spark is a data processing engine compatible with Hadoop. It can perform real-time processing and has an ability to process data in Cassandra, HBase, Hive, HDFS and any Hadoop InputFormat. Spark can also run in Hadoop clusters through its own standalone mode or YARN. Spark supports Scala, Java, and Python.