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SSIS Azure Data Lake Store Destination Mapping Problem

The ProblemMy current project involves Azure’s Data Lake Store and Analytics. We’re using the SSIS Azure Feature Pack’s Azure Data Lake Store Destination to move data from our clients on premise system into the Lake, then using U-SQL to generate a delta file which goes on to be loaded into the warehouse. U-SQL is a “schema-on-read” language, which means you need a consistent and predictable format to be able to define the schema as you pull data out.We ran in to an issue with this schema-on-read approach, but once you understand the issue, it’s simple to rectify. The Data Lake Store Destination task does not use the same column ordering which is shown in the destination mapping. Instead, it appears to rely on an underlying column identifier. This means that if you apply any conversions to a column in the data flow, this column will automatically be placed at the end of file– taking away the predictability of the file format, and potentially making your schema inconsistent if you have historic data in the Lake.An ExampleCreate a simple package which pulls data from a flat file and moves it into the Lake.Mappings of the Destination are as follows:Running the package, and viewing the file in the Lake gives us the following (as we’d expect, based on the mappings):Now add a conversion task – the one in my package just converts Col2 to a DT_I4, update the mappings in the destination, and run the package.Open the file up in the Lake again, and you’ll find that Col2 is now at the end and contains the name of the input column, not the destination column:The FixAs mention in my “The Problem” section, the fix is extremely simple – just handle it in your U-SQL by re-ordering the columns appropriately during extraction! This article is more about giving a heads up and highlighting the problem, than a mind-blowing solution.

How to prepare for 70-766 - Perform Big Data Engineering on Microsoft Cloud Services

There is a new exam currently in beta titled "Perform Big Data Engineering on Microsoft Cloud Services (beta)". With all new exams there is little content on how to revise for the exam beyond the exams summary. This exam however, is what Adatis specialises in! Microsoft may call this "Big Data Engineering" we call it "Modern Data Analytics" and we have a few blogs on the subject. You can sign up to the exam here: https://www.microsoft.com/en-us/learning/exam-70-776.aspx Below you will find links to blog posts by Adatis consultants on topics related to all the key objectives of this exam. I will endeavour to keep this up-to-date with new content added by the team. Good luck with the exam. Design and Implement Complex Event Processing By Using Azure Stream Analytics (15-20%)Streaming data is vital to achieving real-time analytics. The following blogs posts focus on this and offer an introduction and walkthrough for getting started with Stream Analytics. When talking about a wider Lambda approach to Big Data, streaming enables rapid processing via a “Speed” layer.    http://blogs.adatis.co.uk/simonwhiteley/post/Adatis-Hackathon-Jan-2015-Streaming-Analytics-First-Thoughtshttp://blogs.adatis.co.uk/Jose%20Mendes/post/IoT-Hub-Device-Explorer-Stream-Analytics-Visual-Studio-2015-and-Power-BIhttp://blogs.adatis.co.uk/sachatomey/post/2017/01/19/Power-BI-Streaming-Datasets-An-Alternative-PowerShell-Push-Scripthttp://blogs.adatis.co.uk/Jose%20Mendes/post/Data-Data-Revolution Design and Implement Analytics by Using Azure Data Lake (25-30%)Azure Data Lake Store and Analytics are a vital component of the “Modern Data Analytics”. Data which is too large for traditional single server processing needs distributed parallel computation. Rather than pulling data and processing ADLA pushes the processing to the data. Understanding how to process large volumes of data is one part of the “Batch” layer in Lambda http://blogs.adatis.co.uk/ustoldfield/post/data-lakeshttp://blogs.adatis.co.uk/ustoldfield/post/Data-Flow-Job-Execution-in-the-Azure-Data-Lakehttp://blogs.adatis.co.uk/ustoldfield/post/Data-Flow-Pt-2-Vertexes-In-Azure-Data-Lake Design and Implement Azure SQL Data Warehouse Solutions (15-20%)Either as an alternative or in accompaniment to Data Lake is Azure SQL Data Warehouse. If Data Lake is batch across many files, Azure SQLDW is parallel batch over many databases. The key to both services is processing at the storage and not at the compute. The following is an on-going blog series covering the basics all the way to a  deep-dive.   http://blogs.adatis.co.uk/simonwhiteley/post/A-Guide-to-Azure-SQL-DataWarehousehttp://blogs.adatis.co.uk/simonwhiteley/post/Azure-SQLDW-What-is-ithttp://blogs.adatis.co.uk/simonwhiteley/post/Azure-SQLDW-How-Does-Scaling-Workhttp://blogs.adatis.co.uk/simonwhiteley/post/Azure-SQLDW-Distributionhttp://blogs.adatis.co.uk/simonwhiteley/post/Azure-SQLDW-Polybasehttp://blogs.adatis.co.uk/simonwhiteley/post/Azure-SQLDW-Polybase-Design-Patternshttp://blogs.adatis.co.uk/simonwhiteley/post/Azure-SQLDW-Polybase-Limitationshttp://blogs.adatis.co.uk/simonwhiteley/post/Azure-SQLDW-CTAS-Statements Design and Implement Cloud-Based Integration by using Azure Data Factory (15-20%)If you’re looking for a paas solution to move data in Azure, there is only really one option. Azure Data Factory. The following blogs will get you up-to-speed with ADF. http://blogs.adatis.co.uk/terrymccann/post/Getting-started-with-Azure-Data-Factoryhttp://blogs.adatis.co.uk/terrymccann/post/Setting-up-your-first-Azure-Data-Factoryhttp://blogs.adatis.co.uk/terrymccann/post/Azure-Data-Factory-using-the-Copy-Data-task-to-migrate-data-from-on-premise-SQL-Server-to-Blob-storage Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics (20-25%)Know each of the parts is only half the battle, you need to know how, when and why to use each part. What are the best practices? http://blogs.adatis.co.uk/ustoldfield/post/Deploying-a-Hybrid-Cloudhttp://blogs.adatis.co.uk/terrymccann/post/Azure-Data-Factory-Suggested-naming-conventions-and-best-practiceshttp://blogs.adatis.co.uk/ustoldfield/post/Azure-Data-Lake-Store-Storage-and-Best-Practiceshttp://blogs.adatis.co.uk/ustoldfield/post/Shaping-The-Lake-Data-Lake-Framework

Geographic Spatial Analysis with Azure Data Lake Analytics (ADLA)

Whilst working on an Azure Data Lake project, a requirement hit the backlog that could be easily solved with a Geographical Information System (GIS) or even SQL Server - Spatial data type support was introduced into SQL Server 2008. However, Azure Data Lake Analytics (ADLA) does not natively support spatial data analytics so we'll have to extract the data into another service right? Wrong ? :) Due to the extensibility of Azure Data Lake Analytics, we can enhance it to do practically anything. In fact, we can lean on existing components and enhance the service without having to develop the enhancement itself. This blog is a quick run through demonstrating how to enhance ADLA such that it will support Spatial analytics and meet our project requirement. Problem For simplicity I've trivialised the problem. Here's the requirement: Indicate which Bus Stops are within 1.5 km of Southwark Tube Station. To support this requirement, we have two datasets: A list of all the Bus Stops in London, including their Geo location (circa 20k records) The Geo location record of Southwark Tube Station (a single record !) In fact, the location of the tube station is pretty accurate and is geo located to the entrance pavement outside the tube station: This would be an easy problem for a GIS to solve. You would specify the central point i.e. our Southwark Tube station marker and draw a circle, or buffer, with a radius 1.5 km around it and select all bus stops that fall within or intersect with that circle. This spatial analysis is easy for these systems as that's essentially what they are built to do. SQL Server 2008 introduced the Spatial Data Type, this allowed spatial style analysis to be performed on geo data using T-SQL in conjunction with the supplied Geometry and Geography data types. More info on those can be found here So, how can we solve our problem in ADLA, without a GIS and without having to export the data to SQL Server?? Solution You can register existing assemblies with ADLA. It so happens that the SQL Server Data Types and Spatial assemblies are nicely packaged up and can be used directly within ADLA itself - think about that, it's pretty awesome ! Caveat: At the time of writing we have no idea of the licence implications. It will be up to you to ensure you are not in breach :) Those assemblies can be downloaded from here.  You only need to download and install the following file: ENU\x64\SQLSysClrTypes.msi This installs two key assemblies, which you'll need to grab and upload to your Data Lake Store: C:\Program Files (x86)\Microsoft SQL Server\130\SDK\Assemblies\Microsoft.SqlServer.Types.dll C:\Windows\System32\SqlServerSpatial130.dll Once they have been uploaded to your Data Lake Store, you need to register those assemblies with ADLA. DECLARE @ASSEMBLY_PATH string = "/5.UTILITY/USQL-Extend/SQL-Server/"; DECLARE @TYPES_ASM string = @ASSEMBLY_PATH+"Microsoft.SqlServer.Types.dll"; DECLARE @SPATIAL_ASM string = @ASSEMBLY_PATH+"SqlServerSpatial130.dll"; CREATE DATABASE IF NOT EXISTS SQLServerExtensions; USE DATABASE SQLServerExtensions; DROP ASSEMBLY IF EXISTS SqlSpatial; CREATE ASSEMBLY SqlSpatial FROM @TYPES_ASM WITH ADDITIONAL_FILES = ( @SPATIAL_ASM ); Following registration of the assemblies, we can see the registration loaded in the ADLA Catalog database we created: We are now ready to use this U-SQL enhancement in our U-SQL Query - let's go right ahead and solve our problem in one U-SQL Script. // Reference the assemblies we require in our script. // System.Xml we get for free as a System Assembly so we didn't need to register that and our SQLServerExtensions.SqlSpatial assembly REFERENCE SYSTEM ASSEMBLY [System.Xml]; REFERENCE ASSEMBLY SQLServerExtensions.SqlSpatial; // Once the appropriate assemblies are registered, we can alias them using the USING keyword. USING Geometry = Microsoft.SqlServer.Types.SqlGeometry; USING Geography = Microsoft.SqlServer.Types.SqlGeography; USING SqlChars = System.Data.SqlTypes.SqlChars; // First create the centralised point. // In this case it's the pavement outside the entrance of Southwark Tube Station, London. // Format is Longitude, Latitude and then SRID. // NB: It's Longitude then Latitude, that's the opposite way to what you might expect.. DECLARE @southwarkTube Geography = Geography.Point(-0.104777,51.503829,4326); // Next we extract our entire London bus stop data set from the file. // There's about 20k of them. @busStopInput = EXTRACT [StopCode] string, [StopName] string, [Latitude] double?, [Longitude] double? FROM @"/1.RAW/OpenData/Transport/bus-stops-narrow-full-london.csv" USING Extractors.Csv(skipFirstNRows:1,silent:true); // This is effectively the transform step and where the magic happens // Very similar syntax to what you would do in T-SQL. // We are returning all the bus stops that fall within 1500m of Southwark Tube // Essentially we return all stops that intersect with a 1500m buffer around the central tube point @closeBusStops= SELECT * FROM @busStopInput WHERE @southwarkTube.STBuffer(1500).STIntersects(Geography.Point((double)@busStopInput.Longitude,(double)@busStopInput.Latitude,4326)).ToString()=="True"; // The results are written out to a csv file. OUTPUT @closeBusStops TO "/4.LABORATORY/Desks/Sach/spatial-closebusstops.csv" USING Outputters.Csv(outputHeader: true); The query outputs a list of bus stops that are within the specified Spatial distance from Southwark Tube Station. If we have a look at all the bus stops (in red) and overlay all the 'close' bus stops (in green), we can see the results: Pretty neat. Azure Data Lake Analytics does not natively support spatial data analytics but by simply utilising the assemblies that ship with SQL Server, we can extend the capability of U-SQL to provide that functionality or practically any functionality we desire.

Data Flow Pt 2: Vertexes In Azure Data Lake

Following on from my previous post on Job Execution in the Azure Data Lake, this post will explore the concept of Extents and how they are utilised by Vertexes in the actual Job processing in Azure. The U-SQL script that has been authored, compiled and deployed is the logical plan of how the author intends to transform input data into output data. This creates a total amount of work – essentially the amount of data it has to process – which is decomposed into a set of vertexes. Each vertex will process a subset of data, or extents (see Azure Data Lake Storage for more information) and represent a fraction of the total. Vertexes are displayed, or grouped, in a super vertex, also known as a stage. Vertexes in each stage are doing the same operation on a different part of the same data. The number of vertexes in a stage indicates the maximum theoretical parallelisation of that stage. The containers requested for the job will be allocated to complete each vertex. Say there is a 10GB file. This file will be split into 40 Extents and allocated to at least 10 Vertexes. If one wants to process all of the file in parallel then requesting 10 containers will allow for concurrent parallelism. All this is visualised as the job graph.If you have multiple transformations going on in your USQL script this will create multiple stages, and the output of one vertex becomes the input of another vertex in a dependent stage. As a result, dependent stages can begin processing even if preceding stages haven’t completed processing. If dependency does exist, it will create a critical path – which is the dependency chain of vertexes which keep the job running to the very end because the vertex on the bottom depends on the output of the vertex on the top. This can be seen in the Vertex Execution View in the Visual Studio Azure Data Lake Tools view. It’s useful for optimising job, by re-positioning or re-writing elements of your script, by checking which vertex takes the longest. It may not be possible to use all the reserved parallelism during a stage if there are fewer vertexes than Azure Data Lake Analytics Units (ADLAUs) available. For example, if I have 10 ADLAUs – it’s great for an early stage as all ADLAUs will be allocated a vertex. But with later stages, more and more ADLAUs will be idle. So by the last stage only 2 of the 14 are utilised. Unfortunately it is not currently possible to dynamically de-allocate ADLAUs during a job. In conclusion, understanding how vertexes interact with extents and can influence parallelism, you should be able to provision adequate resources for your jobs as well as know where to start the investigation for any long running queries you might have with the Vertex Execution View. In the next post of the series I will be looking into resource costs and optimising spend and time taken to process data.

Azure Data Lake Store : Authorization Failed - The user does not have the permission to perform this operation

Just a quick one as it took slightly longer to solve than it possibly should have done so this might help others with the same problem. I was working fine with an Azure Data Lake Store for a few weeks until yesterday when I couldn’t access the files anymore.  The Azure Portal reported the following:Authorization Failed - The user does not have the permission to perform this operation. It turns out I was accessing it from a new location and the ADLS Firewall needed updating to whitelist my IP Address…. After adding my IP, the Data Lake Store is now accessible again.

Data Flow: Job Execution in the Azure Data Lake

In this blog we will deep dive into the job execution of the Azure Data Lake Analytics (ADLA). If you’ve used ADLA you will have come across the following image: This is the graphical representation of the job execution. But what is is doing at each stage? Preparing The job execution begins with the authoring of the U-SQL script. The U-SQL script itself is the logical plan of how you intend to transform input data into output data. The script gets compiled, which will translate your U-SQL script primarily into C#, as well as a few other items such as XML– which will contain metadata information and the job graph. Once compiled it will create an initial plan – which will then be optimised thus producing an optimised plan. If it fails at this stage it will mostly be down to your U-SQL script failing to compile successfully. Fortunately, the error codes at this stage are fairly helpful and will indicate the line in question. Queued Once compiled and optimised the job progresses to the queued stage. There are a few things which can cause the a job to queue and these are: Running Jobs The default setting per subscription is 3. So if you or someone else in your subscription is already running a series of jobs, your job will be queued until at least one of them completes Higher priority jobs As the queue is ordered by job priority, with loser numbers having a higher priority, other jobs can jump to the top of the queue thus forcing your job to queue for longer Lack of resources Even if there are no running jobs and there are no jobs ahead of yours in the queue, your job will continue to queue if there are not enough Azure Data Lake Analytic Units (ADLAUs) to start the job.   Running Once the job has finished queuing it will run. The Job Manager allocates vertexes, which are collections of data to be processed, to the ADLAUs and uses YARN to orchestrate it. The vertexes will then execute. At this stage, if there is a vertex failure you can download the vertex locally and debug in visual studio. When the vertexes have successfully completed you will be able to consume the end product – either locally, if you have run the job locally, or in the Azure Data Lake Store. Conclusion The above process can be visualised in this graph. Knowing how ADLA executes your job is the first of many steps to being able to write performant U-SQL and debug effectively. Be on the look out for more blogs on the technical deep dive into ADLA!

Testing the Waters: An Overview of Data Science using Azure Data Lakes

Data Science can fit seamlessly within the ecosystem of the data lake, whether this is through HDInsight or the extensibility of Azure Data Lake Analytics and U-SQL. This blog will give a brief overview of what Data Science is; how to link Data Science toolkits to the Azure Data Lake; and best practices for managing the data output from experiments.   Data Science Data Science is the relatively new kid on the block. One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modelling, statistics, analytics, and mathematics.At its core, data science involves using automated methods to analyse massive amounts of data and to extract insight from them. Data science is helping to create new branches of science, and influencing areas of social science and the humanities. The trend is expected to accelerate in the coming years as data from mobile sensors, sophisticated instruments, the web, and more, grows.   Data Science In The Data Lake The nature of the Azure Data Lake Store lends itself to Data Science in that it can hold any data, which the data scientist will want to access, transform and analyse.   HDInsight contains many implementations for data science, such as Spark, R Server and others. Hooking HDInsight to Azure Data Lake Store is pretty simple and follows these steps: In the Azure Portal Marketplace, select HDInsight which will bring up a series of blades. In this blog, I will be using Spark as my cluster type on HDInsight. In the storage settings you can then link your HDInsight cluster to Azure Data Lake Store Confirm your configuration on the next blade and wait around 20 minutes for your cluster to deploy and you’re good to go!   With Azure Data Lake Analytics, you incorporate data science by extending the capabilities of U-SQL and you do this by installing a series of files. Open up your Azure Data Lake Analytics account and click on Sample Scripts at the top. This will bring forward the following blade From there you’ll want to click on the U-SQL Advanced Analytics tab, which will copy about 1.5GB of files to the default Azure Data Lake Store associated to your ADLA account. This will take about 3 minutes to complete. When it’s finished copying the files it will then call a job to register the extension, which can be found in the assemblies folder of the master database. More resources about extensibility of U-SQL can be found here: https://blogs.msdn.microsoft.com/azuredatalake/2017/03/10/using-custom-python-libraries-with-u-sql/ https://blogs.msdn.microsoft.com/azuredatalake/2016/11/22/u-sql-advanced-analytics-introducing-python-extensions-for-u-sql/ The Laboratory Within Azure Data Lake Store, Folder and File Management is incredibly important for a well running data lake. See my blogs on Storage and Best Practices and Shaping The Lake for more information on how to set up your Azure Data Lake Store. The Laboratory is an area to be exclusively used by a data scientist. It’s an area where they can persist the results of experiments and data sets without impacting the day-to-day operations within the data lake or other data scientists. The laboratory is organised in to two broad area: Desks and Exhibits. Desks contain personal workspaces, the contents of which can be organised however the person wishes. It can be as well organised, or disorganised, as the person themselves. The Exhibit contains data sources produced in the Laboratory which are ready to be consumed by other users or systems. Both of which are laid out below. As always, if you have any feedback or comments do let me know!

Azure Data Lake Store–Storage and Best Practices

The Azure Data Lake Store is an integral component for creating a data lake in Azure as it is where data is physically stored in many implementations of a data lake. Under the hood, the Azure Data Lake Store is the Web implementation of the Hadoop Distributed File System (HDFS). Meaning that files are split up and distributed across an array of cheap storage.   What this blog will go into is the physical storage of files in the Azure Data Lake Store and then best practices, which will utilise the framework.   Azure Data Lake Store File Storage As mentioned, the Azure Data Lake Store is the Web implementation of HDFS. Each file you place into the store is split into 250MB chunks called extents. This enables parallel read and write. For availability and reliability, extents are replicated into three copies. As files are split into extents, bigger files have more opportunities for parallelism than smaller files. If you have a file smaller than 250MB it is going to be allocated to one extent and one vertex (which is the work load presented to the Azure Data Lake Analytics), whereas a larger file will be split up across many extents and can be accessed by many vertexes.   The format of the file has a huge implication for the storage and parallelisation. Splittable formats – files which are row oriented, such as CSV – are parallelizable as data does not span extents. Non-splittable formats, however, – files what are not row oriented and data is often delivered in blocks, such as XML or JSON – cannot be parallelized as data spans extents and can only be processed by a single vertex.   In addition to the storage of unstructured data, Azure Data Lake Store also stores structured data in the form of row-oriented, distributed clustered index storage, which can also be partitioned. The data itself is held within the “Catalog” folder of the data lake store, but the metadata is contained in the data lake analytics. For many, working with the structured data in the data lake is very similar to working with SQL databases.   Azure Data Lake Store Best Practices The best practices generally involve the framework as outlined in the following blog: http://blogs.adatis.co.uk/ustoldfield/post/Shaping-The-Lake-Data-Lake-Framework The framework allows you to manage and maintain your data lake. So, when setting up your Azure Data Lake Store you will want to initially create the following folders in your Root Raw is where data is landed in directly from source and the underlying structure will be organised ultimately by Source. Source is categorised by Source Type, which reflects the ultimate source of data and the level of trust one should associate with the data. Within the Source Type, data is further organised by Source System. Within the Source System, the folders are organised by Entity and, if possible, further partitioned using the standard Azure Data Factory Partitioning Pattern of Year > Month > Day etc., as this will allow you to achieve partition elimination using file sets. The folder structure of Enriched and Curated is organised by Destination Data Model. Within each Destination Data Model folder is structured by Destination Entity. Enriched or Curated can either be in the folder structure and / or within the Database.  

Shaping The Lake: Data Lake Framework

The Azure Data Lake has just gone into general availability and the management of Azure Data Lake Store, in particular, can seem daunting especially when dealing with big data. In this blog, I will take you through the risks and challenges of working with data lakes and big data. Then I will take you through a framework we’ve created to help best manage these risks and challenges. If you need a refresh as to what a data lake is and how to create your first Azure Data Lake Store and your first Azure Data Lake Analytics job, please feel free to follow the links. Risks and Challenges of Big Data and Data Lake The challenges posed by big data are as follow: Volume – is the sheer amount of data becoming unmanageable? Variety – Structured tables? Semi-structured JSON? Completely unstructured text dumps? We can normally manage with systems that contain just one of these, but if we’re dealing with a huge mix, it gets very tricky Velocity – How fast is the data coming in? And how fast do we need to get it to the people who need it? Veracity - How do we maintain accuracy, veracity, when the data is of varying volumes, the sources and structures are different and the speed in which they arrive in the Lake are of differing velocities? Managing all four simultaneously is where the challenges begin. 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. There is also data drowning – as the volume of the data tends towards the massive and the velocity only increases over time we are going to see more and more information available via the lake. When it gets to that point, if the lake is not well managed, then users are going to struggle to find what they’re after. The data may all be entirely relevant and accurate, but if users cannot find what they need then there is no value in the lake itself. Essentially, data drowning is when the amount of data is so vast you lose the ability to find what’s in there. If you ignore these challenges, treat the lake like a dumping ground, you will have contaminated your lake and it will no longer be fit for purpose. If no one uses the Data Lake, it’s a pointless endeavour and not worth maintaining. Everyone needs to be working together to ensure the lake stays clean, managed and good for a data dive! Those are the risks and challenges we face with Azure Data Lake. But how do we manage it? The Framework   We’ve carved the lake up into different sections. The key point is that the lake contains all sorts of different data – some that’s sanitised and ready to consume by the business user, some that’s indecipherable raw data that needs careful analysis before it is of use. By ensuring data are carefully managed you can instantly understand the level of preparation that data has undergone. Data flows from left to right – the further left areas represent where data has been input directly from source systems. The horizontal sections describe the level of preparation – Manual, Stream and Batch. Manual – aka, the laboratory. Here data is manually prepared with ad-hoc scripts. Stream – The data here flows in semi-real time, coming from event hubs and being landed after processing through stream-specific tools such as Streaming Analytics. Once landed, there is no further data processing – the lake is essentially a batch processing tool. Batch – This is more traditional data processing, the kind of “ETL” seen by many BI developers. We have a landing area for our raw data, a transitional area where data is cleaned, validated, enriched and augmented with additional sources and calculation, before finally being placed in a curated area where it is ready for consumption by the business. We’re taking the blank-canvas of the Data Lake Store and applying a folder structure, a file management process and a curation process over the top. The folder structure itself can be as detailed as you like, we follow a specific structure ourselves:   The Raw data area, the landing place for any files entering the lake, has sub-folders for each source of data. This allows for the easy browsing of the data sources within the Lake and ensures we are not receiving the same data twice, even if we use it within different systems. The Enriched and Curated layers however, have a specific purpose in mind. We don’t take data and enrich/clean/process it without a business driver, it’s not something we do for fun. We can therefore assign a project or system name to it, at this point it is organised into these end-systems. This means we can view the same structure within Enriched as within Curated. Essentially Raw data is categorised by Source whilst Enriched and Curated data is categorised by Destination. There’s nothing complicated about the Framework we’ve created or the processes we’ve ascribed to it, but it’s incredibly important that everyone is educated on the intent of it and the general purpose of the data lake. If one user doesn’t follow process when adding data, or an ETL developer doesn’t clean up test files, the system starts to fall apart and we succumb to the challenges we discussed at the start. To summarise, structure in your Azure Data Lake Store is key to maintaining order: • You need to enforce and maintain folder structure. • Remember that structure is necessary whether using unstructured data or tables & SQL • Bear in mind that schema on read applies temporary structure – but if you don’t know what you’re looking at, this is going to be very hard to do!

SQL PASS Summit–Day 2

Day 2, Thursday, started off with a keynote from David DeWitt on cloud data warehousing, scalable storage and scalable compute. This set my theme for the majority of the day – which turned out to be big data tech.   My first session was with James Rowland-Jones and Kevin Ngo on sizing Azure SQL Data Warehouse for proposals – essentially answering “how much is this going to cost me?”. There are various factors to consider, which I will blog on separately. I’ve already briefly fed back to members of the team and they’re excited to know what I learnt in more detail.   My second session was about best practices for Big BI which, unfortunately, ended up being a sales pitch and I came away having felt that I’ve didn’t learn anything. There’s a lot of promise for BI in the big data space, so watch this space as we explore Azure SQL Data Warehouse, Azure Data Lake (Store and Analytics), and other big data technology for BI.   The third session was with Michael Rys on Tuning and Optimising U-SQL Queries for Maximum Performance. It was a full on session, learnt loads and took loads of notes. I need time to digest this information as Michael covered off a very complex topic, very quickly. I will, however, be blogging on it in due course.   After an intense third session, I chose a less intense session for the last session of the day: a Q&A with the SQL Engineering team. This was a great opportunity to learn from other users how they’re using SQL. Most users who asked questions were wanting to know about indexing, backups and High Availability.   Tonight – packing, and networking before the last day of PASS tomorrow!

SQL PASS Summit–Day 1

Day 1, Wednesday, technically started on Tuesday with a newbies speed networking event in which we had to rotate through a crowd of 10 other people - introducing ourselves and asking questions about our professional lives. This was awkward to begin with but, as the evening wore on, introducing ourselves to strangers became a lot easier and more normal. We then moved on to the Welcome Reception and then a #SQLKaraoke event. Great opportunities to meet new people from different areas of the world and parts of the community. Wednesday morning proper, began with a keynote from Joseph Sirosh. This keynote from Joseph essentially set the tone and theme for a large part of the conference sessions - Azure, Big Data and the Cortana Intelligence Suite. The first session I attended was on Design Patterns for Azure SQL Database (for which a separate blog will be forthcoming). The next session I attended was about incorporating Azure Data Lake Analytics into a BI environment (again, another blog is in the pipeline). My final session of the day was Going Under the Hood with Azure Data Lake. This was the most insightful session of the day, which has subsequently sparked my brain into Data Lake mode (expect many blogs on this), and went through how Azure Data Lake works as well as how the U-SQL language works and resources are allocated. Tonight - more networking. So far, the community has been so welcoming and I’m very much looking forward to tomorrow where I’ll be learning about Big Data solutions and best practices. I’m also looking forward to sharing all my experiences and learning's with my colleagues and wider SQL Community.