REBACA ANALYTICS

REBACA helps determining the use cases for Big Data services & further crafts a good strategy for your organization. The entire strategy is built in close collaboration with the key stakeholders of your company. This ensures that their requirements are fulfilled & are aligned with big data and analytic solutions.

REBACA’s consultants possess immense experience as big data service provider. They work closely with customers and provide them integrated big data solutions, which encompasses unstructured & structured data and various other transactional data sources. We also suggest big data and analytic solutions that will work the best as per your needs.

Services we offer

Big Data governance

in Kafka-Spark- Cassandra distributed framework

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Log analysis

through RCA (Root Cause Analysis) tool with ELK stack

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ETL & Data Modeling in No SQL

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Statistical data modeling

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OUR TECHNOLOGY OFFERINGS

REBACA’s key capabilities & Technology Landscape:

 

  • Framework : Hadoop 1.0/2.0
  • Hadoop components : HDFS, PIG, Oozie Workflow, SQOOP, HIVE, Zookeeper
  • Programming Model : MapReduce, Apache Storm, Spark with scala
  • Messaging System : Flume & Kafka
  • No SQL Database : HBase, Vertica, MongoDB , Cassandra, MapR DB
  • Query on No SQL DB : Apache drill, Apache Pheonix, Impala ( Cloudera)
  • ETL : Pentaho PDI, Logstash
  • BI/Data Visualization : Tableau/Clickview/Microstrategy, MSBI- SSRS, SSIS, SSAS, Spago, Grafana
  • Log Analysis : Elastic, Logstash & Kibana
  • Streaming : Spark streaming, MapR Streams
  • Big data Monitoring tool : Ambari ( Hortonworks), MCS, Cloudera Manager & Navigator
  • Network Security : Kerberos, Knox

 

Enterprise Hadoop :

  • MapR : CDP ,Converged Data Platform 5.0 Onwards with MCS
  • Cloudera CDH, 5.0 onwards with Impala, cloudera manager & Navigator
  • Hortonworks HDP 2.4 onwards with ambari on AZURE.

 

Statistical Modeling & Machine learning Techniques :
• Predictive analysis using Logistic/Linear/Lasso/Ridge Multivariate Regressions,
• Times Series forecasting using ARIMA & Exponential smoothing
• Decision trees K-Means, KNN for cluster analysis
• Neural Network, SVM
• Sentiment Analytic using NLP : Stanford NLP, Core NLP, Q-dap, open NLP, python NLTK & NLP
• Supervised/Unsupervised Learning, Deep Learning

 

Enterprise Cloud Environment :
• AWS : Amazon Web Services EC2 ( Elastic Compute Cloud ),S3 EBS,( Elastic Block Storage ),EMR ( Elastic Map Reduce )
• Azure : HD Insight
• IBM SoftLayer
• Google Cloud