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Is there any way mssql can notify my python application when any table or row has been updated?

I dont have much knowledge in dbs, but wanted to know if there is any technique by which when i update or insert a specific entry in a table, it should notify my python application to which i can then listen whats updated and then update that particular row, in the data stored in session or some temporary storage.
I need to send data filter and sort calls again n again, so i dont want to fetch whole data from sql, so i decided to keep it local, nd process it from there. But i was worried if in the mean time the db updates, and i could have been passing the same old data to filter requests.
Any suggestions?

rdbs only will be updated by your program's method or function sort of things.
you can just print console or log inside of yours.
if you want to track what updated modified deleted things,
you have to build a another program to able to track the logs for rdbs
thanks.

Track changes in SQL Server tables using Python

I am new to Python and I am trying to figure out to integrate Python into my workload. I have a set of tables within SSMS 2014 that I want to track on a daily basis. If there are any changes to the data elements within each table, I want to track that and log that there was a change to it. I want to have that run automatically every morning without me having to run the script manually. I figured that creating some sort of a script/function in Python would be best to run all the tables that I want against it. Are there any packages, templates, etc out there that work best for this kind of situation? Thanks in advance.

Continuous aggregates over large datasets

I'm trying to think of an algorithm to solve this problem I have. It's not a HW problem, but for a side project I'm working on.
There's a table A that has about (order of) 10^5 rows and adds new in the order of 10^2 every day.
Table B has on the order of 10^6 rows and adds new at 10^3 every day. There's a one to many relation from A to B (many B rows for some row in A).
I was wondering how I could do continuous aggregates for this kind of data. I would like to have a job that runs every ~10mins and does this: For every row in A, find every row in B related to it that were created in the last day, week and month (and then sort by count) and save them in a different DB or cache them.
If this is confusing, here's a practical example: Say table A has Amazon products and table B has product reviews. We would like to show a sorted list of products with highest reviews in the last 4hrs, day, week etc. New products and reviews are added at a fast pace, and we'd like the said list to be as up-to-date as possible.
Current implementation I have is just a for loop (pseudo-code):
result = []
for product in db_products:
reviews = db_reviews(product_id=product.id, create>=some_time)
reviews_count = len(reviews)
result[product]['reviews'] = reviews
result[product]['reviews_count'] = reviews_count
sort(result, by=reviews_count)
return result
I do this every hour, and save the result in a json file to serve. The problem is that this doesn't really scale well, and takes a long time to compute.
So, where could I look to solve this problem?
UPDATE:
Thank you for your answers. But I ended up learning and using Apache Storm.

Summary of requirements
Having two bigger tables in a database, you need regularly creating some aggregates for past time periods (hour, day, week etc.) and store the results in another database.
I will assume, that once a time period is past, there are no changes to related records, in other words, the aggregate for past period has always the same result.
Proposed solution: Luigi
Luigi is framework for plumbing dependent tasks and one of typical uses is calculating aggregates for past periods.
The concept is as follows:
write simple Task instance, which defines required input data, output data (called Target) and process to create the target output.
Tasks can be parametrized, typical parameter is time period (specific day, hour, week etc.)
Luigi can stop tasks in the middle and start later. It will consider any task, for which is target already existing to be completed and will not rerun it (you would have to delete the target content to let it rerun).
In short: if the target exists, the task is done.
This works for multiple types of targets like files in local file system, on hadoop, at AWS S3, and also in database.
To prevent half done results, target implementations take care of atomicity, so e.g. files are first created in temporary location and are moved to final destination just after they are completed.
In databases there are structures to denote, that some database import is completed.
You are free to create your own target implementations (it has to create something and provide method exists to check, the result exists.
Using Luigi for your task
For the task you describe you will probably find everything you need already present. Just few tips:
class luigi.postgres.CopyToTable allowing to store records into Postgres database. The target will automatically create so called "marker table" where it will mark all completed tasks.
There are similar classes for other types of databases, one of them using SqlAlchemy which shall probably cover the database you use, see class luigi.contrib.sqla.CopyToTable
At Luigi doc is working example of importing data into sqlite database
Complete implementation is beyond extend feasible in StackOverflow answer, but I am sure, you will experience following:
The code to do the task is really clear - no boilerplate coding, just write only what has to be done.
nice support for working with time periods - even from command line, see e.g. Efficiently triggering recurring tasks. It even takes care of not going too far in past, to prevent generating too many tasks possibly overloading your servers (default values are very reasonably set and can be changed).
Option to run the task on multiple servers (using central scheduler, which is provided with Luigi implementation).
I have processed huge amounts of XML files with Luigi and also made some tasks, importing aggregated data into database and can recommend it (I am not author of Luigi, I am just happy user).
Speeding up database operations (queries)
If your task suffers from too long execution time to perform the database query, you have few options:
if you are counting reviews per product by Python, consider trying SQL query - it is often much faster. It shall be possible to create SQL query which uses count on proper records and returns directly the number you need. With group by you shall even get summary information for all products in one run.
set up proper index, probably on "reviews" table on "product" and "time period" column. This shall speed up the query, but make sure, it does not slow down inserting new records too much (too many indexes can cause that).
It might happen, that with optimized SQL query you will get working solution even without using Luigi.

Data Warehousing? Summary tables are the right way to go.
Does the data change (once it is written)? If it does, then incrementally updating Summary Tables becomes a challenge. Most DW applications do not have that problem
Update the summary table (day + dimension(s) + count(s) + sum(s)) as you insert into the raw data table(s). Since you are getting only one insert per minute, INSERT INTO SummaryTable ... ON DUPLICATE KEY UPDATE ... would be quite adequate, and simpler than running a script every 10 minutes.
Do any reporting from a summary table, not the raw data (the Fact table). It will be a lot faster.
My Blog on Summary Tables discusses details. (It is aimed at bigger DW applications, but should be useful reading.)

I agree with Rick, summary tables make the most sense for you. Update the summary tables every 10 minutes and just pull data from it, as user's request summaries.
Also, make sure that your DB is indexed properly for performance. I'm sure db_products.id set as a unique index. but, also make sure that db_products.create is defined as a DATE or DATETIME and also indexed since you are using it in your WHERE statement.

Creating an archive - Save results or request them every time?

I'm working on a project that allows users to enter SQL queries with parameters, that SQL query will be executed over a period of time they decide (say every 2 hours for 6 months) and then get the results back to their email address.
They'll get it in the form of an HTML-email message, so what the system basically does is run the queries, and generate HTML that is then sent to the user.
I also want to save those results, so that a user can go on our website and look at previous results.
My question is - what data do I save?
Do I save the SQL query with those parameters (i.e the date parameters, so he can see the results relevant to that specific date). This means that when the user clicks on this specific result, I need to execute the query again.
Save the HTML that was generated back then, and simply display it when the user wishes to see this result?
I'd appreciate it if somebody would explain the pros and cons of each solution, and which one is considered the best & the most efficient.
The archive will probably be 1-2 months old, and I can't really predict the amount of rows each query will return.
Thanks!

Specifically regarding retrieving the results from queries that have been run previously I would suggest saving the results to be able to view later rather than running the queries again and again. The main benefits of this approach are:
You save unnecessary computational work re-running the same queries;
You guarantee that the result set will be the same as the original report. For example if you save just the SQL then the records queried may have changed since the query was last run or records may have been added / deleted.
The disadvantage of this approach is that it will probably use more disk space, but this is unlikely to be an issue unless you have queries returning millions of rows (in which case html is probably not such a good idea anyway).

If I would create such type of application then
I will have some common queries like get by current date,current time , date ranges, time ranges, n others based on my application for the user to select easily.
Some autocompletions for common keywords.
If the data gets changed frequently there is no use saving html, generating new one is good option

The crucial difference is that if data changes, new query will return different result than what was saved some time ago, so you have to decide if the user should get the up to date data or a snapshot of what the data used to be.
If relevant data does not change, it's a matter of whether the queries will be expensive, how many users will run them and how often, then you may decide to save them instead of re-running queries, to improve performance.

Download activity chart flask SQL

I am working on a web application for downloading resources of an unimportant type. It's written in python using the flask web framework. I use the SQLAlchemy DB system.
It has a user authentication system and you can download the resources only while logged in.
What I am trying to do is a download history chart for every resource and every user. To elaborate, each user could see two charts of their download activity on their profile page, for the last 7 days and the last year respectively. Each resource would also have a similar pair of charts, but they would instead visualize how many times the resource itself was downloaded in the time periods.
Here is an example screenshot of the charts
(Don't have enough reputation to embed images)
http://dl.dropbox.com/u/5011799/Selection_049.png
The problem is, I can't seem to figure out what the best way to store the downloads in a database would be. I found 2 ways that are relatively easy to implement and should work:
1) I could store the download count for each day in the last week in separate fields and every 24 hours just get rid of the first one and move them to the left by 1. This, however, seems like a kind of a hacky way to do this.
2) I could also create a separate table for the downloads and every time a user downloads a resource I would insert a row into the table with the Datetime, user_id of the downloader and the resource_id of the downloaded resource. This would allow me to do some nice querying of time periods etc. The problem with that configuration could be the row count in the table. I have no idea how heavily the website is going to be used, but if I do the math with 1000 downloads / day, I am going to end up with over 360k rows in just the first year. I don't know how fast that would to perform. I know I could just archive old entries if performace started being a huge problem.
I would like to know whether the 2nd option would be fast enough for a web app and what configuration you would use.
Thanks in advance.

I recommend the second approach, with periodic aggregation to improve performance.
Storing counts by day will force you to SELECT the existing count so that you can either add to it with an UPDATE statement or know that you need to INSERT a new record. That's two trips to the database on every download. And if things get out of whack, there's really no easy way to determine what happened or what the correct numbers ought to be. (You're not saving information about the individual events.) That's probably not a significant concern for a simple download count, but if this were sensitive information it might matter.
The second approach simply requires a single INSERT for each download, and because each event is stored separately, it's easy to troubleshoot. And, as you point out, you can slice this data any way you like.
As for performance, 360,000 rows is trivial for a modern RDBMS on contemporary hardware, but you do want to make sure you have an index on date, username/resource name or any other columns that will be used to select data.
Still, you might have more volume than you expect, or maybe your DB is iffy (I'm not familiar with SQLAlchemy). To reduce your row count you could create a weekly batch process (yeah, I know, batch ain't dead despite what some people say) during non-peak hours to create summary records by week.
It would probably be easiest to create your summary records in a different table that is simply keyed by week and year, or start/end dates, depending on how you want to use it. After you've generated the weekly summary for a period, you can archive or delete the daily detail records for that period.