Sam Eldin CV - Resume Tools 4 Sharing Architects 2 Show Big Data Presentation Android Training Java-Unix Code Templates Interviews QA & Code

	DataDops DataOps definition on the web is summarized as: 1 DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. 2 DataOps, or data operations, is the latest agile operations methodology to spring from the collective consciousness of IT and big data professionals. It focuses on cultivating data management practices and processes that improve the speed and accuracy of analytics, including data access, quality control, automation, integration, and, ultimately, model deployment and management. The reality is that DataOps is new and there is no clear cut what DataOps is. Therefore, we are charting a new road for DataOps. Our DataOps Definition: DataOps is any data operations or processes which include Big Data, CRM, Analytics, Data Visualizer, Data Minding, Data Storage, Business Intelligence (BI) and Data Security. In a nutshell, DataOps is any data operation which advances and secures your business. Based on our definition, the scope of DataOps would be too vague and too broad to handle. We are proposing what we call DataOps Basic Services. What are the DataOps Engines-Services? A Software engine or an engine is a tool which handles one operation or a task. For example, a file server which handle files storage and access. A Service may use one of more engine to provide one or more service. For example, a data parser as a service may use a file server, databases server, decompression or decryption engines. A data parser may parse files such as text, PDF, Image, or excel sheet. Data with all its types, complexity and size would be handled by a number of our Intelligent Data Services Editors. These editors would be using data engines-services to address all complex features (size, types, structured, non-structured, .. etc). What are Our DataOps Basic Services - DataOps Services? We would be creating Intelligent Data Services Editors to handle the following Services:          1. Collecting          2. Extracting, Transforming and Loading          3. Analyzing          4. Updating          5. Storing          6. Machine Learning - Data Minding          7. Data Parses          8. Data Convertors          9. CRM Matrix (BIT, Indexing and Hashing)          10. Data Mapping          11. Data Modeling          12. Data Compression-Decompression          13. Data Encryption-Decryption          14. Data certification and validations          15. Data Cleansing and pruning          16. Report Generator          17. Data Visualizer          18. Data streaming          19. Analytics          20. Business Intelligence (BI)