Database Migration basically means moving your enterprise application’s data from its current environment or state or version or vendor to a newer one. Reasons could be many behind a database migration, as simple as moving an On-Premise Database to a Cloud-Based Database or moving from one or a legacy system is unable to meet the business needs because of some critical features that are required now is missing in the current database.
Enterprises today are looking at Database Migration mainly to cut cost, adopt a more modern Software setup, adopt Cloud, consolidate and standardize their various silo databases across different applications and platforms.
A database migration project covers multistage processes and would require very meticulous planning and strategy. It would involve Database Assessment, Schema Conversion, Migration, and Testing & Tuning.
Data Replication is the process of having the same data stored in more than one location, like a DC-DR site. In simple terms, it is copying data from one database server to another On-Premise Database Server or Cloud Database Server so as to improve data availability. Making data available across different locations so that it is closer to the users helps in decreasing latency and dramatically increases response time.
Traditionally there are different types of Data Replication and depending on the organization’s business needs, environments, users, locations and other such factors, one can choose the most suited model. Following are some of them:-
- Merge Replication – Going by the name itself, data from more than one database are merged into one single database.
- Snapshot Replication – Snapshot replication is useful when data is not changing frequently and is intermittent and is always a bit slower as compared to transactional replication.
- Transactional Replication – In Transactional replication Data is replicated in real-time from the primary database server to the target database. Here the same order is maintained and replicated as they occur with the primary database and hence transactional consistency is maintained.
BUSINESS INTELLIGENCE (BI)
Business Intelligence is a process involving technology and various tools to analyze data and convert it into meaningful insights in the form of information which is then consumed by business decision-makers. Business Intelligence involves a robust architecture along with a variety of tools, applications and methodologies to collect data from data sources – both internal & external. BI helps organizations in improving decision-making, streamlining internal processes, gaining competitive advantage, increase operational efficiency and revenue.
Business Intelligence would typically cover a number of processes and steps to achieve optimum results. It would generally cover Data Mining, Data Preparation, Statistical Analysis, Data Visualization, Reports & Dashboards, KPI and Querying
Data Science primarily can be defined as extracting information and insights out of data in raw form. Truly speaking Data science is a magic blend of Data Inference, Algorithms, and Analytical Tools in order to get answers to complex and critical problems and questions. In a simpler term, Data Science is all about using data in creative ways to extract business value out of it. Leveraging advanced models, algorithms, tools, and methodologies to segregate the signals from the noise, capture patterns and provide predictive and meaningful insights.
Data Life Cycle
Predictive Intelligence is primarily about collating and analyzing past behavior to predict future performance. The biggest advantage of artificial intelligence is the ability to absorb and analyze massive amounts of data in a fraction of the time it would take a human to do the same.
Cognitive Intelligence mainly involves the use of technologies and tools that would enable apps, bots, and websites to understand the needs of the users through natural language processing (NLP).
Natural Language Processing or NLP as it is popularly called is primarily the capability of a software program to understand human’s natural language. Humans have been interacting with a computer through certain software code and hence making computers directly understand human’s natural language is not that easy and that is where NLP comes in.
Data Ingestion is about moving data from one or more sources to a target destination where it can be seamlessly stored, processed and analyzed. The data could be in different formats and types and hence there is a need for the data to be processed (cleaned and transformed) into a specific format that would allow the user to analyze it. Data Ingestion can happen in Realtime or in Batches or in a way that combines both Realtime and Batch mode.
Big Data is managing of large volumes of data which may be both structured and unstructured to achieve organizational goals, greater insights, better decisions, and business strategy. Today every organization generates and handles petabytes of data and more than the handling such huge volumes of data, what matters is how they use it and take benefit out of it.
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