SQL NOSQL mix possible or not? - sql

I have an application on a relational database that needs to change in order to keep more data. My problem is that just 2 of the tables will store more data(up to billions of entries) and one the tables is "linked" by fk to other tables. I could give up the relational model for these tables.
I'd like to keep the rest of the db intact and changes only these 2 tables. I'm also doing a lot of queries - from simple selects to group by and subqueries - on these tables, so more problems there.
My experience with NoSQL is limited, so I'm asking which one (if any) of its siblings suits my needs:
- huge data
- complex queries
- integration with a SQL database. This is not as important as the first two and I could migrate my entire db to an equivalent if it's worth it.
Thanks

Both relational databases and NoSQL approaches can handle data having billions of data points. With the supplied information, it is hard to make a meaningful and specific recommendation. It would be helpful to know more about what you are trying to do with the data, what your options are regarding your hardware and network topology, etc.
I assume since you are currently using a relational database, you have probably already looked at partitioning or otherwise structuring your larger tables so that your query performance is satisfactory. This activity by itself can be non-trivial, but IMHO, a good database design with optimized sql can take you a very long way before there is a clear need to explore alternatives.
However, if your data usage looks like write-once, read often, the join dependencies are manageable, and you need to perform some aggregations over the data set, then you might start to look into alternative approaches like Hadoop or MongoDB - however these choices come with trade-offs in terms of their performance, capabilities, platform requirements, latency, and so forth. Your particular question about integration between a NoSQL repository and a SQL database at the query level might not be realizable without some duplication of data between the two. For example, MongoDB does not like joins (http://stackoverflow.com/questions/4067197/mongodb-and-joins), so you must design your persistence model with that in mind, and this may involve duplication of data.
The point I am trying to make is - identifying the "right" approach will depend on your specific goal and constraints.

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What is NoSQL? How it works? [duplicate]

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I've been hearing things about NoSQL and that it may eventually become the replacement for SQL DB storage methods due to the fact that DB interaction is often a bottle neck for speed on the web.
So I just have a few questions:
What exactly is it?
How does it work?
Why would it be better than using a SQL Database? And how much better is it?
Is the technology too new to start implementing yet or is it worth taking a look into?
There is no such thing as NoSQL!
NoSQL is a buzzword.
For decades, when people were talking about databases, they meant relational databases. And when people were talking about relational databases, they meant those you control with Edgar F. Codd's Structured Query Language. Storing data in some other way? Madness! Anything else is just flatfiles.
But in the past few years, people started to question this dogma. People wondered if tables with rows and columns are really the only way to represent data. People started thinking and coding, and came up with many new concepts how data could be organized. And they started to create new database systems designed for these new ways of working with data.
The philosophies of all these databases were different. But one thing all these databases had in common, was that the Structured Query Language was no longer a good fit for using them. So each database replaced SQL with their own query languages. And so the term NoSQL was born, as a label for all database technologies which defy the classic relational database model.
So what do NoSQL databases have in common?
Actually, not much.
You often hear phrases like:
NoSQL is scalable!
NoSQL is for BigData!
NoSQL violates ACID!
NoSQL is a glorified key/value store!
Is that true? Well, some of these statements might be true for some databases commonly called NoSQL, but every single one is also false for at least one other. Actually, the only thing NoSQL databases have in common, is that they are databases which do not use SQL. That's it. The only thing that defines them is what sets them apart from each other.
So what sets NoSQL databases apart?
So we made clear that all those databases commonly referred to as NoSQL are too different to evaluate them together. Each of them needs to be evaluated separately to decide if they are a good fit to solve a specific problem. But where do we begin? Thankfully, NoSQL databases can be grouped into certain categories, which are suitable for different use-cases:
Document-oriented
Examples: MongoDB, CouchDB
Strengths: Heterogenous data, working object-oriented, agile development
Their advantage is that they do not require a consistent data structure. They are useful when your requirements and thus your database layout changes constantly, or when you are dealing with datasets which belong together but still look very differently. When you have a lot of tables with two columns called "key" and "value", then these might be worth looking into.
Graph databases
Examples: Neo4j, GiraffeDB.
Strengths: Data Mining
While most NoSQL databases abandon the concept of managing data relations, these databases embrace it even more than those so-called relational databases.
Their focus is at defining data by its relation to other data. When you have a lot of tables with primary keys which are the primary keys of two other tables (and maybe some data describing the relation between them), then these might be something for you.
Key-Value Stores
Examples: Redis, Cassandra, MemcacheDB
Strengths: Fast lookup of values by known keys
They are very simplistic, but that makes them fast and easy to use. When you have no need for stored procedures, constraints, triggers and all those advanced database features and you just want fast storage and retrieval of your data, then those are for you.
Unfortunately they assume that you know exactly what you are looking for. You need the profile of User157641? No problem, will only take microseconds. But what when you want the names of all users who are aged between 16 and 24, have "waffles" as their favorite food and logged in in the last 24 hours? Tough luck. When you don't have a definite and unique key for a specific result, you can't get it out of your K-V store that easily.
Is SQL obsolete?
Some NoSQL proponents claim that their favorite NoSQL database is the new way of doing things, and SQL is a thing of the past.
Are they right?
No, of course they aren't. While there are problems SQL isn't suitable for, it still got its strengths. Lots of data models are simply best represented as a collection of tables which reference each other. Especially because most database programmers were trained for decades to think of data in a relational way, and trying to press this mindset onto a new technology which wasn't made for it rarely ends well.
NoSQL databases aren't a replacement for SQL - they are an alternative.
Most software ecosystems around the different NoSQL databases aren't as mature yet. While there are advances, you still haven't got supplemental tools which are as mature and powerful as those available for popular SQL databases.
Also, there is much more know-how for SQL around. Generations of computer scientists have spent decades of their careers into research focusing on relational databases, and it shows: The literature written about SQL databases and relational data modelling, both practical and theoretical, could fill multiple libraries full of books. How to build a relational database for your data is a topic so well-researched it's hard to find a corner case where there isn't a generally accepted by-the-book best practice.
Most NoSQL databases, on the other hand, are still in their infancy. We are still figuring out the best way to use them.
What exactly is it?
On one hand, a specific system, but it has also become a generic word for a variety of new data storage backends that do not follow the relational DB model.
How does it work?
Each of the systems labelled with the generic name works differently, but the basic idea is to offer better scalability and performance by using DB models that don't support all the functionality of a generic RDBMS, but still enough functionality to be useful. In a way it's like MySQL, which at one time lacked support for transactions but, exactly because of that, managed to outperform other DB systems. If you could write your app in a way that didn't require transactions, it was great.
Why would it be better than using a SQL Database? And how much better is it?
It would be better when your site needs to scale so massively that the best RDBMS running on the best hardware you can afford and optimized as much as possible simply can't keep up with the load. How much better it is depends on the specific use case (lots of update activity combined with lots of joins is very hard on "traditional" RDBMSs) - could well be a factor of 1000 in extreme cases.
Is the technology too new to start implementing yet or is it worth taking a look into?
Depends mainly on what you're trying to achieve. It's certainly mature enough to use. But few applications really need to scale that massively. For most, a traditional RDBMS is sufficient. However, with internet usage becoming more ubiquitous all the time, it's quite likely that applications that do will become more common (though probably not dominant).
Since someone said that my previous post was off-topic, I'll try to compensate :-) NoSQL is not, and never was, intended to be a replacement for more mainstream SQL databases, but a couple of words are in order to get things in the right perspective.
At the very heart of the NoSQL philosophy lies the consideration that, possibly for commercial and portability reasons, SQL engines tend to disregard the tremendous power of the UNIX operating system and its derivatives.
With a filesystem-based database, you can take immediate advantage of the ever-increasing capabilities and power of the underlying operating system, which have been steadily increasing for many years now in accordance with Moore's law. With this approach, many operating-system commands become automatically also "database operators" (think of "ls" "sort", "find" and the other countless UNIX shell utilities).
With this in mind, and a bit of creativity, you can indeed devise a filesystem-based database that is able to overcome the limitations of many common SQL engines, at least for specific usage patterns, which is the whole point behind NoSQL's philosophy, the way I see it.
I run hundreds of web sites and they all use NoSQL to a greater or lesser extent. In fact, they do not host huge amounts of data, but even if some of them did I could probably think of a creative use of NoSQL and the filesystem to overcome any bottlenecks. Something that would likely be more difficult with traditional SQL "jails". I urge you to google for "unix", "manis" and "shaffer" to understand what I mean.
If I recall correctly, it refers to types of databases that don't necessarily follow the relational form. Document databases come to mind, databases without a specific structure, and which don't use SQL as a specific query language.
It's generally better suited to web applications that rely on performance of the database, and don't need more advanced features of Relation Database Engines. For example, a Key->Value store providing a simple query by id interface might be 10-100x faster than the corresponding SQL server implementation, with a lower developer maintenance cost.
One example is this paper for an OLTP Tuple Store, which sacrificed transactions for single threaded processing (no concurrency problem because no concurrency allowed), and kept all data in memory; achieving 10-100x better performance as compared to a similar RDBMS driven system. Basically, it's moving away from the 'One Size Fits All' view of SQL and database systems.
In practice, NoSQL is a database system which supports fast access to large binary objects (docs, jpgs etc) using a key based access strategy. This is a departure from the traditional SQL access which is only good enough for alphanumeric values. Not only the internal storage and access strategy but also the syntax and limitations on the display format restricts the traditional SQL. BLOB implementations of traditional relational databases too suffer from these restrictions.
Behind the scene it is an indirect admission of the failure of the SQL model to support any form of OLTP or support for new dataformats. "Support" means not just store but full access capabilities - programmatic and querywise using the standard model.
Relational enthusiasts were quick to modify the defnition of NoSQL from Not-SQL to Not-Only-SQL to keep SQL still in the picture! This is not good especially when we see that most Java programs today resort to ORM mapping of the underlying relational model. A new concept must have a clearcut definition. Else it will end up like SOA.
The basis of the NoSQL systems lies in the random key - value pair. But this is not new. Traditional database systems like IMS and IDMS did support hashed ramdom keys (without making use of any index) and they still do. In fact IDMS already has a keyword NONSQL where they support SQL access to their older network database which they termed as NONSQL.
It's like Jacuzzi: both a brand and a generic name. It's not just a specific technology, but rather a specific type of technology, in this case referring to large-scale (often sparse) "databases" like Google's BigTable or CouchDB.
NoSQL the actual program appears to be a relational database implemented in awk using flat files on the backend. Though they profess, "NoSQL essentially has no arbitrary limits, and can work where other products can't. For example there is no limit on data field size, the number of columns, or file size" , I don't think it is the large scale database of the future.
As Joel says, massively scalable databases like BigTable or HBase, are much more interesting. GQL is the query language associated with BigTable and App Engine. It's largely SQL tweaked to avoid features Google considers bottle-necks (like joins). However, I haven't heard this referred to as "NoSQL" before.
NoSQL is a database system which doesn't use string based SQL queries to fetch data.
Instead you build queries using an API they will provide, for example Amazon DynamoDB is a good example of a NoSQL database.
NoSQL databases are better for large applications where scalability is important.
Does NoSQL mean non-relational database?
Yes, NoSQL is different from RDBMS and OLAP. It uses looser consistency models than traditional relational databases.
Consistency models are used in distributed systems like distributed shared memory systems or distributed data store.
How it works internally?
NoSQL database systems are often highly optimized for retrieval and appending operations and often offer little functionality beyond record storage (e.g. key-value stores). The reduced run-time flexibility compared to full SQL systems is compensated by marked gains in scalability and performance for certain data models.
It can work on Structured and Unstructured Data. It uses Collections instead of Tables
How do you query such "database"?
Watch SQL vs NoSQL: Battle of the Backends; it explains it all.

SQL versus noSQL (speed)

When people are comparing SQL and noSQL, and concluding the upsides and downsides of each one, what I never hear anyone talking about is the speed.
Isn't performing SQL queries generally faster than performing noSQL queries?
I mean, for me this would be a really obvious conclusion, because you should always be able to find something faster if you know the structure of your database than if you don't.
But people never seem to mention this, so I want to know if my conclusion is right or wrong.
People who tend to use noSQL use it specifically because it fits their use cases. Being divorced from normal RDBMS table relationships and constraints, as well as ACID-ity of data, it's very easy to make it run a lot faster.
Consider Twitter, which uses NoSQL because a user only does very limited things on site, or one exactly - tweet. And concurrency can be considered non-existent since (1) nobody else can modify your tweet and (2) you won't normally be simultaneously tweeting from multiple devices.
The definition of noSQL systems is a very broad one -- a database that doesn't use SQL / is not a RDBMS.
Therefore, the answer to your question is, in short: "it depends".
Some noSQL systems are basically just persistent key/value storages (like Project Voldemort). If your queries are of the type "look up the value for a given key", such a system will (or at least should be) faster that an RDBMS, because it only needs to have a much smaller feature set.
Another popular type of noSQL system is the document database (like CouchDB).
These databases have no predefined data structure.
Their speed advantage relies heavily on denormalization and creating a data layout that is tailored to the queries that you will run on it. For example, for a blog, you could save a blog post in a document together with its comments. This reduces the need for joins and lookups, making your queries faster, but it also could reduce your flexibility regarding queries.
As Einstein would say, speed is relative.
If you need to store a master/detail simple application (like a shopping cart), you would need to do several Insert statements in your SQL application, also you will get a Data set of information when you do a query to get the purchase, if you're using NoSQL, and you're using it well, then you would have all the data for a single order in one simple "record" (document if you use the terms of NoSQL databases like djondb).
So, I really think that the performance of an application can be measured by the number of things it need to do to achieve a single requirement, if you need to do several Inserts to store an order and you only need one simple Insert in a database like djondb then the performance will be 10x faster in the NoSQL world, just because you're using 10 times less calls to the database layer, that's it.
To illustrate my point let me link an example I wrote sometime ago about the differences between NoSQL and SQL data models approach: https://web.archive.org/web/20160510045647/http://djondb.com/blog/nosql-masterdetail-sample/, I know it's a self reference, but basically I wrote it to address this question which I found it's the most challenging question a RDBMS guy could have and it's always a good way to explain why NoSQL is so different from SQL world, and why it will achieve better performance anytime, not because we use "nasa" technology, it's because NoSQL will let the developer do less... and get more, and less code = greater performance.
The answer is: it depends. Generally speaking, the objective of NoSQL DATABASES (no "queries") is scalability. RDBMS usually have some hard limits at some point (I'm talking about millons and millons of rows) where you could not scale any more by traditional means (Replication, clustering, partitioning), and you need something more because your needs keep growing. Or even if you manage to scale, the overall setup is quite complicated. Or you can scale reads, but not writes.
And the queries depends on the particular implementation of your server, the type of query you are doing, the columns in the table, etc... remember that queries are just one part of the RDBMS.
query time of relational database like SQL for 1000 person data is 2000 ms and graph database like neo4j
is 2ms .if you crate more node 1000000 speed stable 2 ms

Advice for hand-written olap-like extractions from relational database

We've implemented over the course of the years a series of web based reports summarizing historical business data (product sales, traffic, etc). The thing relies heavily on complex SQL queries, and the boss expects the results to be real time, but they need up to a minute to execute. The reports are customizable on a several dimensions.
I've done some basic research, and it looks like what we need is some kind of OLAP (?), ETL(?), whatever.
Is that true? Are we supposed to convert to a whole package and trash our beloved developments, or is there a possibility to keep it relational, SQL-based, and get close to a dedicated solution by simply pre-calculating some optimized views with a batch process running at night? Have you got pointers to good documentation on the subject?
Thank you.
You can do ETL (Extract, transform, and load) at night, loading the (probably summarized) data into tables that can usually be queried pretty quickly. Appropriate indexes are still important.
It often makes sense to put those summary tables in a different schema, a different database, or on a different server, but you don't absolutely have to do that.
The structure of the tables is important, and it's not like designing tables for an OLTP system. The IBM Redbooks have a couple of titles that can help you design the tables.
Data Modeling Techniques for Data
Warehousing
Dimensional Modeling: In a Business
Intelligence Environment
Most dbms today support SQL analytic functions. See, for example, Analytic Functions by Example for Oracle, or Window Functions for PostgreSQL.
In the long term, it sounds as though a move to a data warehouse would definitely benefit you (as suggested in Catcall's answer). You can use the existing reports as a starting point for your data warehouse's requirements.
In the short term, you could build summarised tables optimised for your existing reporting requirements. This should probably be regarded as a stopgap, unless you are never going to change these reports again.
You might also benefit from looking into partitioning tables in your database by date/time, since you will probably still want to report the current day's data for realtime reporting purposes.

What makes Cassandra (and NoSQL in general) a better solution to an RDBMS?

Well, NoSQL is a buzzword right now so I've been looking into it. I'm yet to get my head around ColumnFamilies and SuperColumns, etc... But I have been looking at how the data is mapped.
After reading this article, and others, it seems the data is mapped in a JSON like format.
Users = {
1: {
username: "dave",
password: "blahblah",
dateReged: "1/1/1"
},
2: {
username: "etc",
password: "blahblah",
dateReged: "2/1/1",
comment: "this guy has a comment and dave doesns't"
},
}
The RDBMS format would be:
Table name: "Users"
id | username | password | dateReged | comment
---+----------+----------+-----------+--------
1 | dave | blahblah | 1/1/1 |
---+----------+----------+-----------+--------
2 | etc | blahblah | 2/1/1 | this guy has a comment and dave doesn't
Assuming I understand this correctly and my above examples are right, why would I choose the RDBMS design over the NoSQL design? Personally, I'd much rather work with the JSON structure... Does this mean I should choose NoSQL over, say, MySQL?
I guess what I'm asking is "when should I choose NoSQL over RDBMS?"
On a side note, as I've said, I'm still not fully understanding how to go about implementing a Cassandra database. Ie, how do I create the above Users table in a new database? Any tutorials, documentation, etc you could point to would be great. My google'ing hasn't turned up much in terms of 'starting from scratch'...
If you are google, then you might be in a position where a NoSQL would be easier on you than a RDBMS. Since you are not, the many advantages an RDBMS provides you will probably be of some use. Significantly, on a single node, NoSQL offers absolutely no advantages over RDBMSes. RDBMSes offer lots of advantages over NoSQL, though. what are they?
RDBMSes use some pretty deep magic to understand the data it owns, and the data you are asking for, in such a way that it can return that data in the most efficient manner possible. If you didn't ask about some column, the rdbms doesn't waste any effort retrieving it. If you are interested in rows that have fields in common across two tables, (this is a join, btw), the RDBMS doesn't have to check every single pair of rows for matches, or what a NoSQL db usually does is just give you everything and make you do the checking. with a RDBMS, you can usually construct queries that are actually 'about' the data you are using, like "if the date is a tuesday", and if your indexes support it (if you do that query alot then you would add such an index) you can get those rows efficiently.
There is another reason why RDBMSes are nice. Transactions are easy on RDBMSes, but are much harder to get right on NoSQL databases. Supposing you are implementing a blogging engine. Suppose the post title (which appears in the URL) needs to be unique across all posts. In an RDBMS, you can easily be sure that you won't get this wrong accidentally. With a NoSQL database, if it does support some kind of transactional integrity, it's usually at the shard level, anything that could possibly require that kind of integrity must be on the same shard. since any pair of users could possibly be posting at the same moment, then every users' post must be on the same shard to get the same effect. Well, then you don't get any benefit at all from NoSQL.
The main advantage of NoSQL is horizontal scalability and distributed storage. That means you can have a large number of 'cluster nodes' and write to them in parallel. The cluster will ensure changes are propagated to the other cluster nodes eventually (eventual consistency).
NoSQL is not so much about SQL (the term means "not only SQL"). In fact, some NoSQL products do support a subset of SQL. The reason the data format is different (JSON or list of property / value pairs versus tabular data) is: within relational databases, the number of columns (and column names) is defined in a central place, which doesn't work well with horizontal scalability (you would need to stop all cluster nodes for schema changes). Also, joins are not supported as much because that would break horizontal scalability (data from multiple cluster nodes may need to be read, if the data is distributed).
NoSQl databases are fine for some websites where you don't need transaction or consistency where all you are doing is presenting some data (but until you get really really large, they are not really very needed).
But if you need to enforce financial rules (or other complex data integrity rules) or internal controls or reporting and aggregating data for reporting, you need an RDBMS. I'll bet even Google uses RDBMS' for their own HR and financial data, etc.
For some web applications, you might even want a combination of both, the nosql database for some types of information, the transactional relational database for orders and other things where transactional consistency is a must.
If you develop web sites, I think you need to thoroughly understand both types of databases and the needs behind them before choosing how to handle any new functionality.
It seems to me that you have almost no knowledge of relational databases and would rather do what is easier for you personally than what is right for the project. Maybe I'm not reading that correctly, but anyone who never uses joins is suspect in terms of understanding relational databases.
You don't decide between these two based on which one seems easier to understand or which is the buzzword of the month, you decide them based on the functionality you will need, not just for the user interface but for administrative tasks, reporting, financial or other types of data auditing, government regulation, data recovery in case of a hardware failure, etc.
RDBMS' are all about consistency. They do a great job on data that gets churned alot with transactions. See also ACID (atomicity, consistency, isolation, durability). Sometimes you don't need all that, like when storing data from logs or working on data that's not going to change, just accumulate.
NoSQL databases let you relax the requirements for transactions and get better performance (as well as scale to large distributed storage silos easier).
The advantage fo NoSql is that its simpler and if you have your OO blinkers on it fullfills all your persistence needs.
The advantage of SQL based realtional database is that you can easily re-use and extend your data in ways that were not envisaged in the original design. Also "Object" databases tend to perform very badly (even if its possable) when you want to do the equivalent of SQLs aggregate queries like COUNT, SUM, AVG.
Googles BIGTABLE which is the biggest OO database anywhere (and probably the biggest database period) also supports SQL and sql features like indexing and strong typing.
Answer is easy. If you need data storage - use NoSQL, if you need more features then just storing data - use RDBMS.
I guess what I'm asking is "when should I choose NoSQL over RDBMS?"
[Caveat: I've never read about NoSQL before]
According to Wikipedia, NoSQL isn't good at joins: which implies (to me) no referential integrity and no normalization.
As many books about NoSQL mention, it's not about which database is better than the other. It's more what you need.
As everyone say in the other answers, many NoSQL databases support horizontal scalability and are focused on high availability but they are not always the best fit for your needs.
for example, Cassandra is great to add or remove nodes from a cluster, allowing that high scalability. But when you compare Cassandra with MySQL in an environment with just one node (one server), and with no distributed architecture, there isn't a lot of different, since the main advantages of Cassandra are not used.
Now, why should you use SQL? The most common reason is transaction management. Currently, no popular NoSQL database natively supports transactions. You can emulate them, but they are not part of the native functionality as in most SQL databases.
For Cassandra, there is a full and free training in https://academy.datastax.com
There you won't only find trainings to install and configure Cassandra, but to use its tools. It even gives you completion certificates.
Datastax has its own distribution of Cassandra, but it follows all the same guidelines as the Apache project; it offers some extra tools.
The simplest answer I can think of is: When your data doesn't fit a relational model.
I gave a talk at OSCON about when NoSQL can be the right choice, and some of the different sub-categories to be aware of: http://assets.en.oreilly.com/1/event/45/The%20NoSQL%20Ecosystem%20Presentation.pdf
Cassandra in and of itself is not better than an RDBMS. It is better under some circumstances. An RDBMS is vastly superior for transaction processing, master data management, reference data, data warehousing and (some forms of) BI.
Use NOSQL if your application requires a flexible schema, variable length rows, variable types of columns, eventual integrity, horizonal scalability on commodity servers, and high availability achieved by means of a distributed architecture.
NOSQL does not do joins for several reasons: you already joined the data before the NOSQL file was loaded so there is no need to; because a distributed join over far-reaching servers would be resource intensive. The first reason above is simple: you have embedded all the data you need into a single structure. If you do not embed the data and have to link, don't expect great performance out of it. Linking is a euphemism for application-provided joining without the benefit of consolidating the data as a join does. Assuming hashing a key is the method of data distribution, different records that have the same hash key would be collocated. Thereby if joining were permitted, the joined data would all be on the same server.
It's not just black and white.

MySQL design question - which is better, long tables or multiple databases?

So I have an interesting problem that's been the fruit of lots of good discussion in my group at work.
We have some scientific software producing SQLlite files, and this software is basically a black box. We don't control its table designs, formats, etc. It's entirely conceivable that this black box's output could change, and our design needs to be able to handle that.
The SQLlite files are entire databases which our user would like to query across. There are two ways (we see) of implementing this, one, to create a single database and a backend in Python that appends tables from each database to the master database, and two, querying across separate databases' tables and unifying the results in Python.
Both methods run into trouble when the black box produces alters its table structures, say for example renaming a column, splitting up a table, etc. We have to take this into account, and we've discussed translation tables that translate queries of columns from one table format to another.
We're interested in ease of implementation, how well the design handles a change in database/table layout, and speed. Also, a last dimension is how well it would work with existing Python web frameworks (Django doesn't support cross-database queries, and neither does SQLAlchemy, so we know we are in for a lot of programming.)
If you find yourself querying across databases, you should look into consolidating. Cross-database queries are evil.
If your queries are essentially relegated to individual databases, then you may want to stick with multiple databases, as clearly their separation is necessary.
You cannot accommodate arbitrary changes in a database's schema without categorizing and anticipating that change in some way. In the very best case with nontrivial changes, you can sometimes simply ignore new data or tables, in the worst case, your interpretation of the data will entirely break down.
I've encountered similar issues where users need data pivoted out of a normalized schema. The schema does NOT change. However, their required output format requires a fixed number of hierarchical levels. Thus, although the database design accommodates all the changes they want to make, their chosen view of that data cannot be maintained in the face of their changes. Thus it is impossible to maintain the output schema in the face of data change (not even schema change). This is not to say that it's not a valid output or input schema, but that there are limits beyond which their chosen schema cannot be used. At this point, they have to revise the output contract, the pivoting program (which CAN anticipate this and generate new columns) can then have a place to put the data in the output schema.
My point being: the semantics and interpretation of new columns and new tables (or removal of columns and tables which existing logic may depend on) is nontrivial unless new columns or tables can be anticipated in some way. However, in these cases, there are usually good database designs which eliminate those strategies in the first place:
For instance, a particular database schema can contain any number of tables, all with the same structure (although there is no theoretical reason they could not be consolidated into a single table). A particular kind of table could have a set of columns all similarly named (although this "array" violates normalization principles and could be normalized into a commonkey/code/value schema).
Even in a data warehouse ETL situation, a new column is going to have to be determined whether it is a fact or a dimensional attribute, and then if it is a dimensional attribute, which dimension table it is best assigned to. This could somewhat be automated for facts (obvious candidates would be scalars like decimal/numeric) by inspecting the metadata for unmapped columns, altering the DW table (yikes) and then loading appropriately. But for dimensions, I would be very leery of automating somethings like this.
So, in summary, I would say that schema changes in a good normalized database design are the least likely to be able to be accommodated because: 1) the database design already anticipates and accommodates a good deal of change and flexibility and 2) schema changes to such a database design are unlikely to be able to be anticipated very easily. Conversely, schema changes in a poorly normalized database design are actually more easy to anticipate as shortcomings in the database design are more visible.
So, my question to you is: How well-designed is the database you are working from?
You say that you know that you are in for a lot of programming...
I'm not sure about that. I would go for a quick and dirty solution not a 'generic' solution because generic solutions like the entity attribute value model often have a bad performance. Don't do client side joining (unifying the results) inside your Python code because that is very slow. Use SQL for joining, it is designed for that purpose. Users can also make their own reports with all kind of reporting tools that generate sql statements. You don't have to do everything in your app, just start with solving 80% of the problems, not 100%.
If something breaks because something inside the black box changes you can define views for backward compatibility that keeps your app functioning.
Maybe the scientific software will add a lot of new features and maybe it will change its datamodel because of those new features..? That is possible but then you will have to change your application anyways to take profit from those new features.
It sounds to me as if your problem isn't really about MySQL or SQLlite. It's about the sharing of data, and the contract that needs to exist between the supplier of data and the user of the same data.
To the extent that databases exist so that data can be shared, that contract is fundamental to everything about databases. When databases were first being built, and database theory was first being solidified, in the 1960s and 1970s, the sharing of data was the central purpose in building databases. Today, databases are frequently used where files would have served equally well. Your situation may be a case in point.
In your situation, you have a beggar's contract with your data suppliers. They can change the format of the data, and maybe even the semantics, and all you can do is suck it up and deal wth it. This situation is by no means uncommon.
I don't know the specifics of your situation, so what follows could be way off target.
If it was up to me, I would want to build a database that was as generic, as flexible, and as stable as possible, without losing the essential features of structured and managed data. Maybe, some design like star schema would make sense, but I might adopt a very different design if I were actually in your shoes.
This leaves the problem of extracting the data from the databases you are given, transforming the data into the stable format the central database supports, and loading it into the central database. You are right in guessing that this involves a lot of programming. This process, known as "ETL" in data warehousing texts, is not the simplest of programming challenges.
At least ETL collects all the hard problems in one place. Once you have the data loaded into a database that's built for your needs, and not for the needs of your suppliers, turning the data into valuable information should be relatively easy, at least at the programming or SQL level. There are even OLAP tools that make using the data as simple as a video game. There are challenges at that level, but they aren't the same kind of challenges I'm talking about here.
Read up on data warehousing, and especially data marts. The description may seem daunting to you at first, but it can be scaled down to meet your needs.