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BIG DATA ANALYTICS - Helical tech service (article)

BIG DATA ANALYTICS u2013 HELICAL IT SOLUTIONS<br>We at Helical IT Solutions 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 u2013 from data ingestion, data processing, data storage/data warehouse, BI, and analytics, to implementing streaming analytics, etc.<br>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, etc

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BIG DATA ANALYTICS - Helical tech service (article)

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  1. BIG DATA ANALYTICS – HELICAL IT SOLUTIONS We at Helical IT Solutions 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. DATA INGESTION: Data ingestion is the first step in data pipeline and it involves fetching data from one or various data sources into a system wherein it can be stored and analysed. Based on the data- source, data ingestion can be done either in real time (streaming) or in batches. Processing of different batches can be concurrent too. With streaming, as the name suggests, as soon as the data comes in, it is loaded into the target, near real-time. Various factors make data ingestion an extremely complex process including increasing number and variety of data sources, structured and unstructured data, speed of data, identifying and capturing changed data, etc. A good data pipeline involves building data ingestion which is able to handle the above challenges along with taking care of network latency, network bandwidth, etc. 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.

  2. DATA PROCESSING: In data processing, we basically process the data which was ingested. It could involve any of the below: • Data cleaning • Null handling • Data integration from various data source to a single data source • Applying custom business rules • Transformations, etc. 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. Data processing tools can be categorized broadly into two types: 1. In-Memory: Tools like Apache Spark comes under this category. It takes the entire data into a local or distributed RAM memory, and then on top of that processing is done, thus performance is extremely fast. Since the entire data gets loaded into the In-Memory, the hardware requirement is generally on the higher side. These In-Memory tools can further be categorized into centralized and distributed. 2. Filesystem or DB-Driven: In this type of tool, data is stored in the DB itself and only that data is fetched which is required for processing. Thus, often, the performance of these tools is lower as compared to In-Memory tool though the hardware requirement is not that high. We have ample experience with using most of these data processing tools, primarily open source. We can help you achieve your business objectives at a fraction of cost compared with other proprietary processing engines, with same or even better quality. DATA WAREHOUSE: 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. Apart from relational database, big data databases like Hadoop, Druid, Cassandra, Hive, Impala, etc., can also be used for building a data warehouse. There are specialized data warehouse appliances which are columnar in nature, allowing very high speeds during read operations. Example: Ingress, Vertica, Hana, MySQL, DB2, etc. There are also cloud-based DW which could be used such as Amazon Redshift, dash DB, Google Query, Azure SQL, etc. Different kinds of databases and data storage come with their own share of advantages. With concept of polyglot persistence gaining traction, it is now possible to use multiple databases for powering a single application. This helps leveraging the advantage of each database.

  3. We, at Helical, have rich experience in data modelling and data warehousing, and have hands-on experience with various kinds of databases. We can work with you and based upon your data size and performance requirement; we can provide consulting as well as build the data warehouse solution for you. DATA ANALYSIS: Once the data is ready, there are various options which could be used for data analysis. 1. Charting Engines: These engines help in making sense of data by creating reports, dashboards, geographical dashboards, etc. There are free and open-source charting engines available in the market like D3, C3, Provtovis, etc. D3 is one of the most popular charting engines which is also developer-friendly and allows to custom-create new charts. There also exist paid charting engines like Fusion Charts, High Charts, etc. 2. BI Software: A mature BI software can provide sufficiently rich self-service interface allowing users to drag and drop to create reports and dashboards. BI software can also be categorized into open-source BI software (like Helical Insight, Japsersoft, Pentaho, etc.) and proprietary BI software (like Sisense, Tableau, QlikView, etc). BI software can also be segmented as in-memory software or non-in-memory software. 3. Analytics: Various analytical tools like R, Minitab, Python, etc., can be used to implement and derive trends from data. Based upon the use case, various kinds of algorithms or a combination can be used like linear regression, clustering, decision tree, conjoint analysis, etc. 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. DATA PROCESSING

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