Databricks uses DBU's as a costing unit whether based onto of AWS/Azure/GCP and I want to know if Databricks has a google cloud Big Query equivalent of --dry_run for estimating costs? https://cloud.google.com/bigquery/docs/estimate-costs
Azure databricks calculates costs associated with DBU’s as shown in below table
AWS calculates it as shown in the table below
But Unfortunately, Databricks do not have a google cloud Big Query equivalent of --dry_run for estimating costs
Related
I'm trying to get my head around Databricks.
I've found documentation stepping through importing data from S3 or Azure Datalake, and then outputting into Azure Synapse Analytics or another Data Warehouse solution.
After a quick play, I've recognised that you can simply save a table in Databricks, access it using SQL, and even pull it into PowerBI as a source.
So my question: for a small Datamart (10 dims, 5 facts), why would I choose to pay for an additional database solution like Azure SQL, Synapse, RDS or other when I could simply leave the data in a table in Databricks and then access it directly from my reporting tool from there?
Thank you in advance.
Andy
Yes this is very much possible . Just to let you know that SQL Azure and Synapse may be a Microsoft offering but they are for different purpose , Synapse supports MPP and so it more big data implementation . Also its not only how many dimension and fact table you have , how much data you have , what kind of aggregation it has etc becomes decisive .
I'm no MS expert - recently hopped onto the Azure train and apologies in advance if I get some information wrong.
Basically need some input in Azure's architecture utilising Azure Data Factory (as the ETL/ELT tool) and Azure SQL database (as the storage), to a BI output - Power BI. My situation is this;
I have on-premise data sources such as Oracle DB, Oracle Cloud SSAS, MS SQL server db
I'd like to have a MS cloud infrastructure solution for reporting purposes.
No data migration needed - merely pumping on-prem data onto cloud and producing a BI reporting solution
Based on my limited knowledge and Google research, Azure Data Factory caters for all my on-prem sources, as well as the future cloud Azure SQL database. If future analysis is needed, Azure Storage and Azure Databricks can be added in to this architecture. I have sketched out the architecture of my proposed solution.
Just confirming my understanding
Without Azure Storage & Databricks (the 2 pink boxes), the 2 Azure component (DF & SQL database) is sufficient to take data from on-premise sources, process on cloud & output into Power BI.
With Azure Storage & Databricks (the 2 pink boxes), processing will be more efficient as their summarised function is to store training data models & act as an analytics processing engine.
Azure SQL database is more suitable, as compared to Azure SQL datawarehouse as my data sources does not exceed 1TB; cost-wise is cheaper AND one of my data sources contain data from call centers, hence OLTP is more suitable. Plus I have Azure Databricks to support the analytical bit that SQL datawarehouse does (OLAP).
Any other comments to help me understand this whole architecture will be great!
I am a new learner of Azure. I was wondering if we have #Query (value="...") kind or any equivalence for DocumentDb (CosmosDB). Because, the documentDB does not take #Query. I am looking to convert the sql query (From jpa to cosmosDB).
Taking data from on-prem or IaaS sources like SQL on a VM, Oracle etc, requires a Self-Hosted Integration Runtime (SHIR).
Please review the Modern Data Warehouse pattern which sounds similar to what you are proposing.
I would like some advice/tips about the right technology to select in order to store some forecast data on Azure technologies.
My team and I are scraping some weather forecast data everyday from various sources and store it as is on a Azure File Storage. The files format is "grib2" which is a standard format of weather forecast data.
We are able to extract the data from those "grib2" files using python script running on a Azure VM.
We now have several files that represent hundreds gigabytes of data to store and I'm struggling to find which data store from the Azure technologies suits the best our needs in term of praticity and cost.
We started using "Azure Table Storage" first because it's cheap solution,
but I've read on many posts that it is a bit old and not very adapted to our solution as it for example does not allow more than 1,000 entites per query and no aggregation on data.
I considered using Azure SQL db but it seems that it can become very expensive very fast.
I also considered the Azure Data Lake Storage Gen2 (and HDinsight) technologies but am not very at ease with those blob storages and am not really able to say if it can suit my needs in terms of praticity and if it is "easy to query".
By now we just plan to achieve that :
1) Extract data from grib2 files thanks to a python script running on an Azure VM
2) Insert the transformed data into [Azure storage]
3) Query the [Azure storage] from Azure Machine Learning Service or a local R script (for example)
4) Insert the computed data into [Azure storage]
where [Azure Storage] technology is to determine.
Any help or advice would be much appreciated, thanks.
A couple of things I would see here:
To store the downloaded files in raw format (grib2 in your case), either place them on good ol' Azure Blob Storage. Cheap storage exactly for your needs.
Use Azure Databricks to load the data from the storage account and unpack it into memory. (python or scala)
Load the data in memory - still in Databricks - to run you ML inferencing. You could also use SparkR if you really want to.
Store the computed files in a serving layer. This really depends on what you want to do with it later. Often Azure SQL Database is an obvious choice. There is a native Spark connector which efficiently writes data from Databricks to SQL DB.
In addition to using Databricks as your inferencing environment, it's also a good choice for ML training (e.g. utilizing Azure ML Service).
I am planning to write a windows service that comsumes the twitter streaming api to save tweets and related information (sentiment score, twitter-user, date-of-creation) of a specific topic into an azure storage. I need a way to query these information later like "show me the average sentiment score of tweets in the last 24h" therefore SQL or LINQ must be available.
Some numbers:
Number of tweets saved per day approx. 20.000
Save data for 3 month (20.000 tweets * 90 days)
Data saved: tweet text (140 chars), sentiment score, twitter user name, date (maybe some more properties)
Saving frequency: Since I am using the streaming api, I get tweets in real time which have to be saved into the storage.
Query frequency: About every 30 minutes.
I wonder which Azure Storage is suited for this purpose. I think I have to decide between Azure Table Storage and SQL database.
There are two things to consider choosing between these 2
1. Price:
SQL Azure: check the calculator: https://azure.microsoft.com/en-us/pricing/calculator/
Storage table: check the calculator: https://azure.microsoft.com/en-us/pricing/calculator/?service=storage
You should consider Capacity and Transactions and the service tier to see which one is cheaper...
2. Performance:
If you design it right, in many cases table storage should be faster than Sql Azure because of its no-sql/denormalized nature but it probably depends on the queries that you are going to write for it.
In SQL Azure you will use TSQL but in Table storage you will use C# and Linq to query the data...
if you look at #David's comment below there will be limitations based on the queries you are interested in if you use Table Storage, so you have to be aware of those limitations in Table Storage as well ...
Currently my team is creating a solution that would use HDInsight. We will be getting 5TB of data daily and will need to do some map/reduce jobs on this data. Would there be any performance/cost difference if our data will be stored in Azure Table Storage instead of Azure HBase?
The main differences will be in both functionality and cost.
Azure Table Storage doesn't have a map reduce engine attached to it in itself, though of course you could use the map reduce approach to write your own.
You can use Azure HDInsight to connect Map Reduce to table storage. There are a couple of connectors around, including one written by me which is hive focused and requires some configuration, and may not suit your partition scheme (http://www.simonellistonball.com/technology/hadoop-hive-inputformat-azure-tables/) and a less performance focused, but more complete version from someone at Microsoft (http://blogs.msdn.com/b/mostlytrue/archive/2014/04/04/analyzing-azure-table-storage-data-with-hdinsight.aspx).
The main advantage of Table Storage is that you aren't constantly taking processing cost.
If you use HBase, you will need to run a full cluster all the time, so there is a cost disadvantage, however, you will get some functionality and performance gains, plus you will have something a bit more portable, should you wish to use other hadoop platforms. You would also have access to a much greater range of analytic functionality with the HBase option.
HDInsight (HBase/Hadoop) uses Azure Blob storage not ATS. For your data-storage you will charged only applicable blob storage cost, based on your subscription.
P.S. Don't forget to delete your cluster once job has completed, to avoid charges. Your data will persist in BLOB storage and can be used by next cluster you build.