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BigData Analysis

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Big Data Analytics

We at SVM Technologies Pvt Ltd believe that success of Big Data projects lies not only in its implementation but also in its analysis to establish a system that drives change in the processes of the organization. We can help you create magic with big data – from data ingestion, data processing, data storage/data warehouse, BI, and analytics, to implementing streaming analytics, etc. Note that to build your data pipeline, we could use your heterogeneous and multiple data sources. We have experience with various open source tools, ETL tools, NoSQL databases, popular Apache products as well as proprietary products which can be used for any of the above operations. Get in touch with us to learn about our capabilities, skillsets, use cases and demo of Big Data analysis.

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Assisting Customers Across Diverse Initiatives

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Data Ingestion

Data ingestion is the first step in data pipeline and it involves fetching the data from one or various data sources into a system wherein it can be stored and further analyzed. We are experienced in various types of data ingestion tools – proprietary as well as open source. Some ETL tools we work with are Talend / Pentaho Data integrator, Apache Flume, Apache Flink, Apache Spark, Kafka, Nifi, Sqoop, kylo, etc.

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Data Processing

There are various tools which could be used for data processing. Open Source ETL tools like Talend, Pentaho data integrator (PDI) could be used for data processing. Codes could be written using Python or Java as well. Besides these, there are tools like Apache Spark, Flume, Flink, sqoop, apache storm, etc., which could be used for data processing. We have ample experience with using most of these data processing tools, primarily open source.

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Data Warehouse / Big Data

After data is processed, it is loaded into target database. This target database could be a relational database, a big data database or a data warehouse appliance. Relational database could be used for a set amount of data, but with increasing data, it has its own challenges and limitations. There are also cloud-based DW which could be used such as Amazon Redshift, dashDB, Google Query, Azure SQL, etc.

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Data Analysis

Based on your requirement, budget, timeline and self-service capability requirement, we could help you in finding a data analysis tool that will be a perfect fit for your organization. We are also experienced with various kinds of data analytics algorithms and could build prediction models based on your use case. These engines help in making sense of data by creating reports, dashboards, geographical dashboards, etc.

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