So to give a concrete example, if the default file system was
/user/filename.txtwould resolve to
Why does the default file system matter? The first answer to this is purely convenience. It is a heck lot easier to simply say
adl://amitadls.azuredatalakestore.net/in code and configurations. Secondly, many components in Hadoop use relative paths by default. For instance there are a fixed set of places, specified by relative paths, where various applications generate their log files. Finally, many ISV applications running on Hadoop specify important locations by relative paths.
Read on to see how.
Most common patterns using Azure Data Lake Store (ADLS) involve customers ingesting and storing raw data into ADLS. This data is then cooked and prepared by analytic workloads like Azure Data Lake Analytics and HDInsight. Once cooked this data is then explored using engines like Azure SQL Data Warehouse. One key pain point for customers is having to wait for a substantial time after the data was cooked to be able to explore it and gather insights. This was because the data stored in ADLS would have to be loaded into SQL Data Warehouse using tools row-by-row insertion. But now, you don’t have to wait that long anymore. With the new SQL Data Warehouse PolyBase support for ADLS, you will now be able to load and access the cooked data rapidly and lessen your time to start performing interactive analytics. PolyBase support will allow to you access unstructured/semi-structured files in ADLS faster because of a highly scalable loading design. You can load the files stored in ADLS into SQL Data Warehouse to perform analytics with fast response times or you use can the files in ADLS as external tables. So get ready to unlock the value stored in your petabytes of data stored in ADLS.
I’ve been waiting for this support, and I’m happy that they were able to integrate the two products.
Late last year, I presented a Cognitive Intelligence demo using Azure Data Lake (ADL) at PASS Summit keynote. It was a fun and quick demo! Watch it here
In case you’re new to ADL, you can now (since Dec 2015) develop, compile and run ADL locally in Visual Studio. This is huge! Because you don’t have to worry about your ADL Analytics Unit (AU) consumptions. Plus, this allows you to try it before you buy it too!
Click through for the step-by-step installation instructions.
The answer is sampling, we don’t bring in 100% of the data, but maybe 10%, or 1%, or even 0.01%, it depends how much you need to reduce your dataset. It is however critical to know how to sample data correctly in order to maintain a level of accuracy of data in your reports.
Option 1: Take the top x rows of data
Don’t do it. Ever. Just no.
What if the source data you’ve been given is pre-sorted by product or region, you’d end up with only data from products starting with ‘a’, which would give you some wildly unpredictable results.
Option 2: Take a random % sample
Now we’re talking. This option will take, for example 1 in every 100 rows of data, so it’s picking up an even distribution of data throughout the dataset. This seems a much better option, so how do we do it?
Read on for a couple of sampling methods.
Today early adopters of Amazon Athena are using it for big data analytics pipeline projects along with Kinesis streaming data and other Amazon data sources.
Athena is serverless parallel query pay-per-use service. There is no infrastructure to set up or manage. It scales automatically and can handle large datasets or complex distributed queries.
The easy way of thinking about Athena is that it’s ElasticMapReduce (a pay-as-you-go Hadoop cluster) without the ceremony of administering or spinning up the cluster.
It’s important to note that Athena is not a general purpose database. Under the hood is Presto, a query execution engine that runs on top of the Hadoop stack. Athena’s purpose is to ask questions rather than insert records quickly or update random records with low latency.
That being said, Presto’s performance, given it can work on some of the world’s largest datasets, is impressive. Presto is used daily by analysts at Facebook on their multi-petabyte data warehouse so the fact that such a powerful tool is available via a simple web interface with no servers to manage is pretty amazing to say the least.
Athena is Amazon’s response to Azure Data Lake Analytics. Check out Mark’s blog post for a good way of getting started with Athena.
Recently, we launched Amazon Athena as an interactive query service to analyze data on Amazon S3. With Amazon Athena there are no clusters to manage and tune, no infrastructure to setup or manage, and customers pay only for the queries they run. Athena is able to query many file types straight from S3. This flexibility gives you the ability to interact easily with your datasets, whether they are in a raw text format (CSV/JSON) or specialized formats (e.g. Parquet). By being able to flexibly query different types of data sources, researchers can more rapidly progress through the data exploration phase for discovery. Additionally, researchers don’t have to know nuances of managing and running a big data system. This makes Athena an excellent complement to data warehousing on Amazon Redshift and big data analytics on Amazon EMR.
In this post, I discuss how to prepare genomic data for analysis with Amazon Athena as well as demonstrating how Athena is well-adapted to address common genomics query paradigms. I use the Thousand Genomes dataset hosted on Amazon S3, a seminal genomics study, to demonstrate these approaches. All code that is used as part of this post is available in our GitHub repository.
This feels a lot like a data lake PaaS process where they’re spinning up a Hadoop cluster in the background, but one which you won’t need to manage. Cf. Azure Data Lake Analytics.
Separating storage from processing can also help to reduce the cost of your data lake. Until you choose to analyze your data, you need to pay only for S3 storage. This model also makes it easier to attribute costs to individual projects. With the correct tagging policy in place, you can allocate the costs to each of your analytical projects based on the infrastructure that they consume. In turn, this makes it easy to work out which projects provide most value to your organization.
The data lake stores metadata in both DynamoDB and Amazon ES. DynamoDB is used as the system of record. Each change of metadata that you make is saved, so you have a complete audit trail of how your package has changed over time. You can see this on the data lake console by choosing History in the package view:
Having a competitor in the data lake space is a good thing for us, though based on this intro post, it seems that Amazon and Microsoft are taking different approaches to the data lake, where Microsoft wants you to stay in the data lake (e.g., writing U-SQL or Python statements to query the data lake) and Amazon wants you to shop the data lake and check out the specific S3 buckets and files for your own processing.
This week, were are now making announcing even more support for Python. As of today Python is now a first-class language supported by our management SDKs. This enables you to develop applications or automate the Data Lake services. Check out or Getting Started articles that now include many python samples
Saveen has a Jupyter notebook which demonstrates Python in Azure Data Lake Store.
It is very easy to treat a data lake as a dumping ground for anything and everything. Microsoft’s sale pitch says exactly this – “Storage is cheap, Store everything!!”. We tend to agree – but if the data is completely malformed, inaccurate, out of date or completely unintelligible, then it’s no use at all and will confuse anyone trying to make sense of the data. This will essentially create a data swamp, which no one will want to go into. Bad data & poorly managed files erode trust in the lake as a source of information. Dumping is bad.
This is how you get data swamps (a term which I’m so happy is catching on). Read the whole thing.