Microsoft Access is a database management system from Microsoft that combines the relational Microsoft Jet Database Engine with a graphical user interface and software-development tools.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
Microsoft Access can be easily implemented with training. It doesn't require expert level skill for basic reporting functions - but can be scaled to a complex database with sophisticated users. Its appropriate to consider if excel needs to be used to create reports, or if there are data entry needs - with corresponding reports.
Microsoft Access has not really changed at all for several years. It might be nice to see some upgrades and changes.
The help info is often not helpful. Need more tutorials for Microsoft Access to show how to do specific things.
Be careful naming objects such as tables, forms, etc. Names that are too long can get cut off in dialog boxes to choose a table, form, report, etc. So, I wish they would have resizable dialog boxes to allow you to see objects with long names.
I wish it could show me objects that are not in use in the database for current queries, tables, reports, forms, and macros. That way unused objects can be deleted without worrying about losing a report or query because you deleted the underlying object.
I and the rest of my team will renew our Microsoft Access in the future because we use and maintain many different applications and databases created using Microsoft Access so we will need to maintain them in the future. Additionally, it is a standard at our place of work so it is at $0 cost to us to use. Another reason for renewing Microsoft Access is that we just don' t have the resources needed to extend into a network of users so we need to remain a single-desktop application at this time.
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
Microsoft Access is easy to use. It is compatible with spreadsheets. It is a very good data management tool. There is scope to save a large amount of data in one place. For using this database, one does not need much training, can be shared among multiple users. This database has to sort and filtering features which seem to be very useful.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
While I have never contacted Microsoft directly for product support, for some reason there's a real prejudice against MS Access among most IT support professionals. They are usually discouraging when it comes to using MS Access. Most of this is due to their lack of understanding of MS Access and how it can improve one's productivity. If Microsoft invested more resources towards enhancing and promoting the use of MS Access then maybe things would be different.
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
Crystal is easier for report writing, but isn't a database solution. Salesforce is lovely, but much more expensive than an old copy of Microsoft Office. For a small budget, [Microsoft] Access was really the only viable option. I only wish it was easier to write complex reports.
Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
