I have a question about an approach of a solution in Azure. The question is how to decide what technologies to use and how to find the best combination of them.
Let's suppose i have two data sets, which are growing daily:
I have a CSV file which comes daily to my ADL store and it contains weather data for all possible Lattitudes and Longtitudes combinations and zip codes for them, together with 50 different weather variables.
I have another dataset with POS (point of sales), which also comes as a daily CSV file to my ADL storage. It contains sales data for all retail locations.
The desired output is to have the files "shredded" in a way that the data is prepared for AzureML forecasting of sales based on weather, and the forecasting is done per retail location and delivered via PowerBI dashboard to each one of them. A requirement is not to allow different location see the forecasts for any other locations.
My questions are:
How do I choose the right set of technologies?
how do I append the incoming daily data?
How do I create a separate ML forecasting results for each location?
Any general guidance on the architecture topic is appreciated, and any more specific ideas on comparison of different suitable solutions is also appreciated.
This is way to broad of a question.
I will only answer your ADL specific question #2 and give you a hint on #3 that is not related to Azure ML (since I don't know what that format is):
If you just use files, add date/time information to your file path name (either in folder or filename). Then use U-SQL File sets to query the ranges you are interested in. If you use U-SQL Tables, use PARTITIONED BY. For more details look in the U-SQL Reference documentation.
If you need to create more than one file as output, you have two options:
a. you know all file names, write an OUTPUT statement for each file selecting only the relevant data for it.
b. you have to dynamically generate a script and then execute it. Similar to this.
Related
I am working for a start up where they are getting excel files from different companies with customer information. We do not have any ETL tool at present as the work is handled manually to transform the data into required structure and load into CRM system.
My plan is to load these excel files into a database and also replicate CRM into a database and do some fuzzy maping.
Can you please recommend a light weight ETL tool to apply a few rules to clean the data and compare the existing customer data that we have?
Thanks,
mc
Getting Excel feeds is certainly very common, and you need a good process for ingesting and validating them, especially since they are often manually created or tweaked, leading to frequent data and formatting issues. Adding insult to injury, Excel has a very fuzzy concept of data types, often throwing spanners in the works.
Where possible, switch your data sources to other formats (JSON, CSV, database extract). This requires upstream work but so does troubleshooting feed issues, so switching to a better format (and defining the feed well!) pays off for both sides fairly quickly.
Process Incoming Files Example describes a general approach for reliably handling multiple feeds of incoming files, with processing and archiving of successful and failing files. The example uses my company's actionETL cross-platform .NET ETL library, but I've also used the same approach previously with other ETL tools.
Map out all current and upcoming data sources and destinations, and see which tools are a good fit. Try before you buy with your actual ETL feeds and requirements. Expect the ETL data integration to be an ongoing project since feeds and requirements never stop changing and growing.
Cheers,
Kristian
I frequently use OBIEE to create custom analyses from predefined subject areas. I'm often making extensive use of filters as I'm typically pulling data on a very specific issue from a huge dataset. A problem I have repeatedly come across is trying to recreate a previous analyses complete with the filter variables.
Obviously if it's something I know I'll come back too, I'll save the query. But that's often not the case. Maybe a month or two will go by and I'll need to generate a new version of a previous report to compare with the original and I end up not being able to trust the output because I'm not sure that it's using the same variables.
Is there a way to append the query and filter variables to the report itself so it can be easily referenced and recreated?
Additional info.
* I almost exclusively output the data from OBIEE as a .csv and do most of the work from excel pivot tables I save in .xlsx.
* I'm not a DBA.
a) You can always save filters as presentation catalog objects instead of clicking them together from zero every single time. Think LEGO bricks for adults. OBIEE is based on the concept of stored and reusable objects.
b) You will never have your filters in your CSV output since CSV is a raw data output and not a formatted / graphical one. If you were using a graphical analysis export you could always add the "Filters View" to your results.
Long story short since you're using OBIEE as a data dump tool and are circumventing what the tool is designed to do and how it is supposed to function you're constraining yourself in terms of the benefits and the functionality you can get from it.
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I thought the whole point of using a Data Lake versus a Data Warehouse was to invert the ETL (Extract, Transform, Load) process to LET (Load, Extract, Transform). Doesn't extracting this data, transforming and loading it into a table get us right back where we started?
IMHO the point of a data lake is to store all types of data: unstructured, semi-structured and structured. The Azure version of this is Azure Data Lake Store (ADLS) and its primary function is scalable, high-volume storage.
Separately, there is a product Azure Data Lake Analytics (ADLA). This analytics product can interact with ADLS, but also blob storage, SQL on a VM (IaaS) and the two PaaS database offerings, SQL Database and SQL Data Warehouse and HDInsight. It has a powerful batch language called U-SQL, a combination of SQL and .net for interrogating and manipulating these data stores. It also has a database option which, where appropriate, allows you to store data you have processed in table format.
One example might be where you have some unstructured data in your lake, you run your batch output and want to store the structured intermediate output. This is where you might store the output in an ADLA database table. I tend to use them where I can prove I can get a performance improvement out of them and/or want to take advantage of the different indexing options.
I do not tend to think of these as warehouse tables because they don't interact well with other products yet, ie they don't as yet have endpoints / aren't visible, eg Azure Data Factory can't move tables from there yet.
Finally I tend to think of ADLS as analogous to HDFS and U-SQL/ADLA as analogous to Spark.
HTH
By definition a data lake is a huge repository storing raw data in it's native format until needed. Lakes use a flat architecture rather than nested (http://searchaws.techtarget.com/definition/data-lake). Data in the lake has a unique ID and metadata tags, which are used in queries.
So data lakes can store structured, semi-structured and unstructured data. Structured data would include SQL database type data in tables with rows and columns. Semi-structured would be CSV files and the like. And unstructured data is anything and everything -- emails, PDFs, video, binary. It's that ID and the metadata tags that help users find data inside the lake.
To keep a data lake manageable, successful implementers rotate, archive or purge data from the lake on a regular basis. Otherwise it becomes what some have called a "data swamp", basically a graveyard of data.
The traditional ELT process is better suited to data warehouses because they are more structured and data in a warehouse is there for a purpose. Data lakes, being less structured, are more suited to other approaches such as ELT (Extract, Load, Transform), because they store raw data that is only categorized by each query. (See this article by Panopoly for a discussion of ELT vs ETL.) For example, you want to see customer data from 2010. When you query a data lake for that you will get everything from accounting data, CRM records and even emails from 2010. You cannot analyze that data until it has been transformed into usable formats where the common denominators are customers + 2010.
To me, the answer is "money" and "resources"
(and probably correlated to use of Excel to consume data :) )
I've been through a few migrations from RDBMS to Hadoop/Azure platforms and it comes down to the cost/budget and use-cases:
1) Port legacy reporting systems to new architectures
2) Skillset of end-users who will consume the data to drive business value
3) The type of data being processed by the end user
4) Skillset of support staff who will support the end users
5) Whether the purpose of migration is to reduce infrastructure support costs, or enable new capabilities.
Some more details for a few of the above:
Legacy reporting systems often are based either on some analytics software or homegrown system that, over time, has a deeply embedded expectation for clean, governed, structured, strongly-typed data. Switching out the backend system often requires publishing the exact same structures to avoid replacing the entire analytics solution and code base.
Skillsets are a primary concern as well, because your often talking about hundreds to thousands of folks who are used to using Excel, with some knowing SQL. Few end-users, in my experience, and few Analysts I've worked with know how to program. Statisticians and Data Engineers tend towards R/Python. And developers with Java/C# experience tend towards Scala/Python.
Data Types are a clincher for what tool is right for the job... but here you have a big conflict, because there are folks who understand how to work with "Data Rectangles" (e.g. dataframes/tabular data), and those who know how to work with other formats. However, I still find folks consistently turning semi-structured/binary/unstructured data into a table as soon as they need to get a result operationalized... because support is hard to find for Spark.
I am working with Azure and have a number of sql databases on such.
I am looking to transfer data between databases on such. I have been doing some research and have found that azure data factory is a method that can be used to achieve. However, I found it difficult to find information on this.
Could someone point me in the direction of using data factory for taking data from db1, transform and massage it and then insert in to db2?
If you simply COPY data from source A to source B, ADF is a good option for you. There is a rich set of supported sources and destinations.
To quickly try it out, you can use COPY wizard, which is code-free. https://learn.microsoft.com/en-us/azure/data-factory/data-factory-copy-wizard
For more details about COPY activity, you may look at this. https://learn.microsoft.com/en-us/azure/data-factory/data-factory-data-movement-activities
When you said "massage data", I don't know what exactly it will be, but ADF Custom Activity and Stored procedure Activity should mostly meet your need.
Does Azure Data Lake Analytics and U-SQL support the notion of Cursors in scripts?
I have a data set that contains paths to further data sets I would like to extract and I want to output the results to separate files.
At the moment I can't seem to find a solution for dynamically extracting and outputting data based on values inside data sets.
U-SQL currently expect that files are known at compile time. Thus, you cannot do extraction or outputting based on locations provided inside a file.
You can specify filesets in the EXTRACT statement that will be somewhat data driven. We are currently working on adding the ability to use filesets on OUTPUT as well.
You can file feature requests at http://aka.ms/adlfeedback.
Cheers
Michael
You might be able to write a Processor to iterate over the rows in the primary dataset. However, you might not be able to access the additional datasets in the Processor.
Another work around might be to concatenate all the additional datasets and perform a join with the primary dataset.