Real-Time Weather With HDF

Balaji Kandregula shows how to use Hortonworks Data Flow components to process weather events in real time:

It’s live weather reporting using HDF, Kafka, and Solr.

Here are the environment requirements for implementing:

  • HDF (for HDF 2.0, you need Java 1.8).
  • Kafka.
  • Spark.
  • Solr.
  • Banana.

Now let’s get on to the steps!

There are a lot of moving parts there, but the pieces do plug in well enough and there are a lot of screen shots to guide you along the way.

Data Lake Zoning

Parth Patel, et al, explain that there ought to be several zones of data within a data lake:

Within a Data Lake, zones allow the logical and/or physical separation of data that keeps the environment secure, organized, and Agile. Typically, the use of 3 or 4 zones is encouraged, but fewer or more may be leveraged. A generic 4-zone system might include the following:

  1. Transient Zone — Used to hold ephemeral data, such as temporary copies, streaming spools, or other short-lived data before being ingested.
  2. Raw Zone – The zone in which raw data will be maintained. This is also the zone where sensitive data must be encrypted, tokenized, or otherwise secured.
  3. Trusted Zone – After Data Quality, Validation, or other processing is performed on data in the Raw Zone, it becomes the “source of truth” in this zone for downstream systems.
  4. Refined Zone – Manipulated and enriched data is kept in this zone. This is used to store the output from tools like Hive or external tools that will write into to the Data Lake.

Your particular situation may differ but I’d consider this to be good advice no matter where or how you’re storing data, such as a classical data warehouse or an ODS.

The Birthday Problem

Mala Mahadevan explains the Birthday problem and demonstrates it with SQL and R:

Given a room of 23 random people, what are chances that two or more of them have the same birthday? 

This problem is a little different from the earlier ones, where we actually knew what the probability in each situation was.

What are chances that two people do NOT share the same birthday? Let us exclude leap years for now..chances that two people do not share the same birthday is 364/365, since one person’s birthday is already a given. In a group of 23 people, there are 253 possible pairs (23*22)/2. So the chances of no two people sharing a birthday is 364/365 multiplied 253 times. The chances of two people sharing a birthday, then, per basics of probability, is 1 – this.

The funny thing for me is that I’ve had the Birthday problem explained three separate times using as a demo the 20-30 people in the classroom.  In none of those three cases was there a match, so although I understand that it is correct and how it is correct, the 100% failure to replicate led a little nagging voice in the back of my mind to discount it.

How Query Store And Plan Guides Interact

Grant Fritchey shows that query metadata gets a little weird when you have a plan guide trying to use one particular query and Query Store is forcing a different plan:

If we rerun the query and then take a look at the first operator in the execution plan, we can see that the Plan Guide is in use… and that the query hash has changed. It no longer matches the original query. Now it matches the query that included the query hint. This actually makes perfect sense. The Plan Guide is basically changing the query from the first example above, into the second.

Now, what happens when we toss in the Query Store

The query behavior is exactly what you want, but some of the metadata is no longer correct.

PySpark Persistence

David Crook shows how to save data to disk from PySpark:

This is working on HDInsight v3.5 w/Spark 2.0 and Azure Data Lake Storage as the underlying storage system.  What is nice about this is that my cluster only has access to its cluster section of the folder structure.  I have the structure root/clusters/dasciencecluster.  This particular cluster starts at dasciencecluster, while other clusters may start somewhere else.  Therefor my data is saved to root/clusters/dasciencecluster/data/open_data/RF_Model.txt

It’s pretty easy to do, and the Scala code would look suspiciously similar.  The Java version of the code would be seven pages long.

Time Brush Custom Visual

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the Time Brush Power BI Custom Visual.  The Time Brush gives you the ability both filter your report and see a graphics representation of your data at the same time. The name Time Brush comes from the behavior used when you select the values you’d like to filter.

The use of color is an interesting take on combining continuous data points with categorical representations of those points.

CI With SQL Server And Jenkins

Chris Adkin shows how to auto-deploy SQL Server Data Tools projects to a SQL Server instance using Jenkins:

The aim of this blog post is twofold, it is to explain how:

  • A “Self building pipeline” for the deployment of a SQL Server Data Tools project can be implemented using open source tools
  • A build pipeline can be augmented using PowerShell

What You Will Need

  • Jenkins automation server

  • cURL

  • SQL Server 2016 (any edition will suffice)

  • Visual Studio 2015 community edition

  • A windows server, physical or virtual to install all of the above on, I will be using Windows Server 2012 R2 as the operating system

Automated integration via CI is extremely helpful, and Chris makes it look easy in this post.

Benefits Of Deprecated Data Types

Raul Gonzalez shows how to get one of the benefits of older, deprecated data types using (MAX) data types:

We can see that our table is managed by two different allocation units, IN_ROW_DATA and LOB_DATA, which means that all data within columns of the data types above, will end up in different pages by default, regardless of the size of the data.

This is the default behaviour for old LOB types, to be stored separately, but new LOB types (MAX) by default will try to get them In-Row if they are small enough to fit.

Having some of those documents In-Row will result in a serious increase in the number of pages to scan, therefore affecting performance.

Note that for the table scan we have used only the IN_ROW_DATA pages, making it much lighter than if we have to scan the sum of all pages.

This might be helpful for some situations, like where you rarely need to get to the LOB data.

Killing SPIDs

Garland MacNeill is all out of bubble gum:

Recently came across a situation where reporting logins were interfering with nightly jobs due to blocking. After a number of attempts of trying to resolve the blocking, it was decided that a stored procedure that disabled the login and killed the user sessions was the most pragmatic solution. This is the code I came up with to resolve the issue.

Click through for the script.  This is definitely a last-ditch option, but it’s good to have in your bag of tricks.

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