I'm building an Azure IoT Hub application. I have several customers, each with a set of devices. Do you think all those customers should be connected to the same hub or a different one(s)?
I would like to populate a multi tenant db (single db, multiple schemas) via azure stream analytics. The idea is to use a job that partitions the data by customer and saves it in a table of a specific schema (schema associated to a specific customer) on my db. It's possible to do it, or the only way to keep customer data separate is to have several db's (instead of having one db and multiple schemas)?
I'm building an Azure IoT Hub application. I have several customers,
each with a set of devices. Do you think all those customers should be
connected to the same hub or a different one(s)?
It really depends on the data which is processed and also your actual requirements. If sharing the IoT Hub resource details with other customers is not an issue, then you can use the same IoT Hub. Else, choose individual IoT Hubs.
I would like to populate a multi tenant db (single db, multiple
schemas) via azure stream analytics. The idea is to use a job that
partitions the data by customer and saves it in a table of a specific
schema (schema associated to a specific customer) on my db. It's
possible to do it, or the only way to keep customer data separate is
to have several db's (instead of having one db and multiple schemas)?
SQL output in Azure Stream Analytics supports writing in parallel as an option. This option allows for fully parallel job topologies, where multiple output partitions are writing to the destination table in parallel. Enabling this option in Azure Stream Analytics however may not be sufficient to achieve higher throughputs, as it depends significantly on your database configuration and table schema. The choice of indexes, clustering key, index fill factor, and compression have an impact on the time to load tables. For more information about how to optimize your database to improve query and load performance based on internal benchmarks, see SQL Database performance guidance. Ordering of writes is not guaranteed when writing in parallel to SQL Database.
See Increase throughput performance to Azure SQL Database from Azure Stream Analytics for more details.
I have a scenario where event hub gets data in every 10 seconds, which pass to the stream analytics and then which is passed to the Azure SQL Server. The technical team raised the concerns that Azure SQL is unable to handler so much of data, if data raises 2,00,00,000. then it stops to work.
Can you please guide me is it actual problem of Azure SQL, if it is then can you please suggest me the solution.
Keep in mind that 4TB is the absolute maximum size of an Azure SQL Premium instance. If you plan to store all events for your use case, then this will fill up very quickly. Consider using CosmosDb or Event Hub Capture if you really need to store the messages indefinitely and use SQL for aggregates after processing with SQL DW or ADLS.
Remeber that to optimise Event Hubs you must have a partitioning strategy to optimise the throughput. See the docs.
First time using Azure. I have a basic node js bot built with Microsofts Bot Framework, and deployed on Azure. What are my options for storage?
I will most likely just be needing simple key:value storage. Mongodb was my first though but I dont think Azure supports it nativeley.
That said, what are my options for storage on Azure? I usual shy away from MySQL just from preference, but theres no actual reason that wouldnt work either.
Take a look at Azure Table Storage for a NoSql solution
Table storage is a key/attribute store with a schemaless design. Because Table storage is schemaless, it's easy to adapt your data as the needs of your application evolve. Access to data is fast and cost-effective for all kinds of applications. Table storage is typically significantly lower in cost than traditional SQL for similar volumes of data.
I need to consider a database to store large volumes of data. Though my initial requirement is to simply retrieve chunks of data and save them in excel file, I am expecting more complex use cases for this data in future where the data will be consumed by different applications especially for analytics - hence need to use aggregated queries.
I am open to use either cloud based storage or on-premises storage. I am considering azure storage table (when there is a need to use aggregated data, I can have a wrapper service + cache around azure table storage but eventually will end up with nosql type storage) and on-premises MongoDB. Can someone suggest pros and cons of saving large data in azure table storage Vs on-premises MongoDB/couchbase/ravendb? Cost factor can be ignored.
I suspect this question may end up getting closed due to its broad nature and potential for gathering more opinions than fact. That said:
This is really going to be an app-specific architecture issue, dealing with latency and bandwidth, as well as the need to maintain on-premises servers and other resources. On-prem, you'll have full control of your hardware resources, but if you're doing high-volume querying against your database, from the cloud, your performance will be hampered by latency and bandwidth. Cloud-based storage (whether in MongoDB or any other database) will have the advantage of being neighbors with your app if set up in the same data center.
Keep in mind: Any persistent database store will need to back its data in Azure Storage, meaning a mounted disk backed by Blob storage. You'll need to deal with the 1TB-per-disk size limit (expanding to 16TB on an 8-core box via stripe), and you'll need to compare this to your storage needs. If you need to go beyond 16TB, you'll need to either shard, go with 200TB Table storage, or go with on-prem MongoDB. But... MongoDB and Table Storage are two different beasts, one being document-based with a focus on query strength, the other a key/value store with very high speed discrete lookups. Comparing the two on the notion of on-prem vs cloud is secondary (in my opinion) to comparing functionality as it relates to your app.
I am at the planning stage of a web application that will be hosted in Azure with ASP.NET for the web site and Silverlight within the site for a rich user experience. Should I use Azure Tables or SQL Azure for storing my application data?
Azure Table Storage appears to be less expensive than SQL Azure. It is also more highly scalable than SQL Azure.
SQL Azure is easier to work with if you've been doing a lot of relational database work. If you were porting an application that was already using a SQL database, then moving it to SQL Azure would be the obvious choice, but that's the only situation where I would recommend it.
The main limitation on Azure Tables is the lack of secondary indexes. This was announced at PDC '09 and is currently listed as coming soon, but there hasn't been any time-frame announcement. (See http://windowsazure.uservoice.com/forums/34192-windows-azure-feature-voting/suggestions/396314-support-secondary-indexes?ref=title)
I've seen the proposed use of a hybrid system where you use table and blob storage for the bulk of your data, but use SQL Azure for indexes, searching and filtering. However, I haven't had a chance to try that solution yet myself.
Once the secondary indexes are added to table storage, it will essentially be a cloud based NoSQL system and will be much more useful than it is now.
Despite similar names SQL Azure Tables and Table Storage have very little in common.
Here are a two links that might help you:
Table Storage, a 100x cost factor
Fat Entities on Table Storage
Basically, the first question should wonder about is Does my app really need to scale? If not, then go for SQL Azure.
For those trying to decide between the two options, be sure to factor reporting requirements into the equation. SQL Azure Reporting and other reporting products support SQL Azure out of the box. If you need to generate complex or flexible reports, you'll probably want to avoid Table Storage.
Azure tables are cheaper, simpler and scale better than SQL Azure. SQL Azure is a managed SQL environment, multi-tenant in nature, so you should analyze if your performance requirements are fit for SQL Azure. A premium version of SQL Azure has been announced and is in preview as of this writing (see HERE).
I think the decisive factors to decide between SQL Azure and Azure tables are the following:
Do you need to do complex joins and use secondary indexes? If yes, SQL Azure is the best option.
Do you need stored procedures? If yes, SQL Azure.
Do you need auto-scaling capabilities? Azure tables is the best option.
Rows within an Azure table cannot exceed 4MB in size. If you need to store large data within a row, it is better to store it in blob storage and reference the blob's URI in the table row.
Do you need to store massive amounts of semi-structured data? If yes, Azure tables are advantageous.
Although Azure tables are tremendously beneficial in terms of simplicity and cost, there are some limitations that need to be taken into account. Please see HERE for some initial guidance.
One other consideration is latency. There used to be a site that Microsoft ran with microbenchmarks on throughput and latency of various object sizes with table store and SQL Azure. Since that site's no longer available, I'll just give you a rough approximation from what I recall. Table store tends to have much higher throughput than SQL Azure. SQL Azure tends to have lower latency (by as much as 1/5th).
It's already been mentioned that table store is easy to scale. However, SQL Azure can scale as well with Federations. Note that Federations (effectively sharding) adds a lot of complexity to your application. I'm also not sure how much Federations affects performance, but I imagine there's some overhead.
If business continuity is a priority, consider that with Azure Storage you get cheap geo-replication by default. With SQL Azure, you can accomplish something similar but with more effort with SQL Data Sync. Note that SQL Data Sync also incurs performance overhead since it requires triggers on all of your tables to watch for data changes.
I realize this is an old question, but still a very valid one, so I'm adding my reply to it.
CoderDennis and others have pointed out some of the facts - Azure Tables is cheaper, and Azure Tables can be much larger, more efficient etc. If you are 100% sure you will stick with Azure, go with Tables.
However this assumes you have already decided on Azure. By using Azure Tables, you are locking yourself into the Azure platform. It means writing code very specific to Azure Tables that is not just going to port over to Amazon, you will have to rewrite those areas of your code. On the other hand programming for a SQL database with LINQ will port over much more easily to another cloud service.
This may not be an issue if you've already decided on your cloud platform.
I suggest looking at Azure Cache in combination with Azure Table. Table alone has 200-300ms latencies, with occasional spikes higher, which can significantly slow down response times / UI interactivity. Cache + Table seems to be a winning combination, for me.
For your question, I want to talk about how to decide with logic choose SQL Table and which need to use Azure Table.
As we know SQL Table is a relational database engine. but if you have a big data in one table the SQL Table is not applicable, because SQL query get big data is slow.
At this time you can choose Azure Table, the Azure Table query is so fast then SQL Table for big data, for example, in our website, someone subscribed many articles, we make the article as feed to user, every user have a copy of article title and description, so in the article table there are lots of data, if we use SQL Table, each query execution maybe take more than 30 seconds. But in Azure Table get users article feed by PartitionKey and RowKey is so fast.
From this example you may know how to choose between in SQL Table and Azure Table.
I wonder whether we are going to end up with some "vendor independent" cloud api libraries in due course?
I think that you have first to define what your application usage funnels are. Will your data model be subjected to frequent changes or it is a stable one? You have to be able to perform ultra fast inserts and reads are not so complicated? Do you need advance google like search? Storing BLOBS?
Those are the questions (and not only) that you have to ask and answer yourself in order to decide if you are more likely going to use NoSql or SQL approach in storing your data.
Please consider that both approaches can easily coexist and can be extended with BLOB storage as well.
Both Azure Tables and SQL Azure are two different beasts.Both are meant for different scenarios, one con to azure table is that you cannot move from azure to any other platform, unless you write providers in your code that can handle such shifts.