![]() In this case, the values of analytics are almost next to zero. Without a data pipeline, the data cannot get updated when the database changes, which makes the analytics(dashboards, charts, etc) static and are only for one-time use. Some ETL tools actually address this by laying out analytical steps graphically, and progressively. It’s harder to explore a dataset when it’s all hidden on a server. Advanced analytics require data pipelines Otherwise, there is no way for you to do sequential data transformation/cleaning through those database GUIs. Not only do you need to understand how to code, but you also need to put the queries all together and make sure they will work. Second, the nature of the SQL interface makes it difficult to cleanse the data due to the learning curve of the SQL and the mechanism of coding. You need to write queries to unveil the data. First, you cannot see what the data table looks like at the first glance. Most database GUIs allow you to manipulate data by SQL queries: these SQL queries, however, are not intuitive and do not provide instant feedback. Database GUIs are not built for data preparation/cleansing With distributed computing however, you can time-share your processing with hundreds of computers on cloud and therefore process terabytes of data. Since you can only configure one server for a SQL database, your querying speed is limited. If you have tens of millions of data, querying it can take longer than 30 seconds and lead to server connection timeout. The reasons can be summarized as the following, Dedicated servers have limits when it comes to processing big data For database GUIs on the other hand, they are not built for cleaning, transforming or modeling data. Data analysis process: Investigation, Cleansing, Transformation, Modeling Why are database GUIs not built for analytics?įor a good data analysis tool, it needs to support the 4 steps above efficiently. Ultimately the goal is to support decision-making. The process includes investigating, cleaning, transforming, and modeling data. Data analysis is essentially a process of digesting and manifesting information. If you’re using MongoDB Compass, MySQL workbench, pgAdmin or some other visual database management tools, you probably wonder why it is so hard to use them for advanced data analytics.įirst, let us define what analytics are.
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