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70-768 Developing SQL Data Models

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Test Number : 70-768
Test Name : Developing SQL Data Models
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: 37 Dumps Questions

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Developing SQL Data Models book

Azure records Lake Analytics and U-SQL | 70-768 Dumps and Real exam Questions with VCE Practice Test

Key Takeaways
  • Azure information Lake Analytics, along with Azure records Lake Storage, is a key part of Microsoft’s Azure records Lake solution. 
  • presently, Azure statistics Lake Analytics can be used for batch workloads best. For streaming and adventure processing workloads, alternate large statistics analytics options on Azure like HDInsight or Azure Databricks may still be used.
  • Azure facts Lake Analytics introduces a new big statistics query and processing language known as U-SQL.
  • U-SQL combines the ideas and constructs both of SQL and C#; the vigour of U-SQL comes from the simplicity and declarative nature of SQL with the programmatic vigor of C# together with prosperous types and expressions.
  • U-SQL operates on unstructured statistics stored in information and provides a schematized view on true of it. It additionally offers a typical metadata catalog system very comparable to relational databases for structured facts. 
  • although huge information and Hadoop applied sciences are greater than a decade historical now, massive statistics and massive information analytics are more vital than ever. whereas the preliminary version of Hadoop become most effective in a position to deal with batch workloads, now Hadoop ecosystem has tools for different use circumstances like structured records, streaming information, event processing, laptop learning workloads and graph processing.

    whereas Hadoop ecosystem has a bunch of equipment like Hive, Impala, Pig, Storm, and Mahout to deliver the comprehensive set of aspects, more moderen records analytics framework like Spark have an integrated approach to handle different types of workloads.

    Azure statistics Lake Analytics, or ADLA, is among the newer large records analytics engines. ADLA is Microsoft’s fully managed, on-demand analytics carrier on Azure cloud. along side Azure facts Lake Storage and HDInsight, Azure statistics Lake Analytics kinds the finished cloud hosted information lake and analytics providing from Microsoft. Azure facts Lake Analytics introduces a brand new big facts query and processing language known as U-SQL. this text gives an outline of U-SQL language and how to make use of it in applications.

    Azure data Lake

    Azure statistics Lake is Microsoft’s statistics lake offering on Azure public cloud and is constructed from distinct functions together with data storage, processing, analytics and other complementary features like NoSQL shop, relational database, facts warehouse and ETL tools.

    Storage functions
  • Azure records Lake Storage or ADLS - Azure statistics Lake Storage is a scalable cloud storage purposely constructed for analytics, according to open HDFS general.
  • Azure Blob Storage – typical purpose, managed object storage for Azure.
  • Analytics & Processing capabilities
  • Azure facts Lake Analytics or ADLA– thoroughly managed, on-demand analytics provider on Azure cloud. supports new U-SQL huge information processing language apart from .web, R and Python.
  • HDInsight– HDInsight offers managed Hadoop clusters operating on Azure and is according to Hortonworks statistics Platform (HDP) Hadoop distro. helps Hadoop ecosystem equipment together with Spark, Hive, Map in the reduction of, HBase, Storm, and Kafka.
  • Azure Databricks– Managed serverless analytics carrier according to Azure Spark. supports a Jupyter/ iPython/Zeppelin like laptop experience, together with other verbal exchange features, and supports Scala, Python, R and SQL.
  • Complementary features
  • Cosmos DB – The managed, serverless, multi modal NoSQL database provider on Azure.
  • Azure SQL Database – A managed, relational database as a service/DBaaS on Azure.
  • Azure SQL Datawarehouse – Cloud-based commercial enterprise records Warehouse (EDW) answer operating on Azure. It uses well-known disbursed techniques and records warehousing concepts like vastly Parallel Processing (MPP), columnar storage, compression and many others. to be certain quick performance for complicated queries.
  • Azure analysis carrier – a totally managed analytics engine on Azure; helps to build semantic models on the cloud. It’s constructed on well-known SQL Server evaluation Server which is an on-premise analytics engine in response to SQL Server. As of now, Azure analysis provider handiest helps Tabular models and does not guide Multidimensional models (remember cubes?).
  • Azure information manufacturing facility – A cloud-primarily based ETL and statistics integration carrier. It’s serverless and provides out-of-the-field connectors to 50+ cloud or on-premise techniques/functions like Azure Blob Storage, Cosmos DB, Azure SQL Database, on prem SQL Server/MySQL/PostgreSQL and even 3rd party capabilities like SFDC, Dropbox and so on. it may well circulation records between cloud functions, from on-premise programs to cloud or vice versa.  
  • figure 1 below shows these quite a few cloud choices from Microsoft on Azure cloud.

    [Click on the image to enlarge it]figure 1: services in Azure records Lake offering

    The massive statistics and facts lake-based mostly application structure on Azure cloud platform is shown below in determine 2.

    [Click on the image to enlarge it]

    figure 2: commonplace massive records/information lake/ETL/analytics architecture on Azure

    U-SQL Introduction

    U-SQL is the big statistics query and processing language for Azure statistics Lake Analytics. It’s a brand new language created by using Microsoft in particular for Azure records Lake Analytics. U-SQL combines SQL-like declarative language with the programmatic vigor of C#, together with C# wealthy varieties and expressions. U-SQL offers the ordinary large records processing concepts equivalent to "schema on examine", "lazy comparison", customized processors and reducers. information engineers who've previously used languages like Pig, Hive and Spark would find similarity with those. builders with C# and SQL expertise would locate U-SQL effortless to study and start with.

     figure 3: How U-SQL relates to C# and SQL

    even though U-SQL makes use of many ideas and key terms from SQL language, it’s no longer ANSI SQL compliant. It provides unstructured file dealing with capabilities the use of key terms like EXTRACT and OUTPUT.at the moment, ADLA and U-SQL may also be used for batch processing handiest. It doesn’t deliver flow analytics or adventure processing skill.

    U-SQL concepts and Scripts
  • U-SQL question and processing logic is written in data with ".usql" extension known as U-SQL scripts. visual Studio IDE or Azure portal could be used for authoring these scripts. A U-SQL mission in visible Studio carries diverse scripts, code in the back of info and connected reference assemblies.
  • figure four under suggests a screenshot of a U-SQL project in visual Studio IDE.

     determine four: A U-SQL mission in visible Studio

  • U-SQL scripts comply with the generic Extract/Retrieve, radically change and cargo/Output demo (ETL) used by means of other big data languages like Pig or Spark. it may extract statistics from text data (each unstructured text data and semi structured info like JSON or XML) and tables.
  • U-SQL imposes a schema whereas retrieving unstructured records from information – this helps in performing SQL-like operations of retrieved statistics.
  • Rowset is the fundamental information constitution of U-SQL. It’s used throughout for extracting information from enter file/table, and performing transformation, as well as for writing to output vacation spot. Rowsets are unordered which helps Azure facts Analytics Engine to parallelize the processing the use of numerous processing nodes.
  • U-SQL scripts can use types, operators and expressions from C#.
  • U-SQL scripts use SQL constructs like select, where, join and different facts definition (DDL) and data manipulation language (DML). All key phrases should be written in upper case handiest.
  • supports control move constructs like IF ELSE, but don't aid whereas or For loop.
  • [Click on the image to enlarge it]

    determine 5: statistics stream in U-SQL script

    what's required for U-SQL native construction

    Microsoft provides an emulator-like setup for attempting U-SQL and Azure statistics Lake on native desktop or desktop. For this, three components are required:

  • visible Studio 2017 or 2019
  • Azure SDK (version 2.7.1 or better) which comes up with the customer aspect SDKs to engage with Azure cloud functions and required for storage, compute and so on.
  • Azure records Lake and stream Analytics tools for visible Studio (version 2.four), which is a plugin for local U-SQL and Azure records Lake building. when you install this, principal Azure records Lake Analytics (and other) assignment templates can be brought in visual Studio as shown under. choose U-SQL task to beginning.
  • [Click on the image to enlarge it]

    figure 6: New task template screenshot

    First U-SQL script

    For the first U-SQL script, they would use a dataset that constitutes of the ranking of restaurants in Bangalore, India. The uncooked statistics are in CSV info and have here columns:

  • rest_id - enjoyable identification of the restaurant
  • identify - name of the restaurant
  • tackle - tackle of the restaurant
  • online_order - no matter if on-line ordering is obtainable within the restaurant or not
  • book_table - even if desk booking alternate options can be found or no longer
  • rate - normal score of the restaurant out of 5
  • votes - complete number of rankings for the restaurant
  • cellphone - phone variety of the restaurant
  • place - nearby during which the restaurant is observed
  • rest_type - class of restaurant (e.g. casual eating, brief Bites, start, Bakery, Dessert Parlor and so on.)
  • favorite_dish_id - id of the most favorite dish of the restaurant
  • The below desk shows the demo facts.

    [Click on the image to enlarge it]determine 7: Restaurant rankings table with pattern records

    The below script reads restaurant rankings information from a CSV file and writes the equal statistics to a TSV file. It doesn’t use a transformation step yet.

    // Script - RestaurantScript.usql // facts is extracted from input file (CSV) and kept in employees rowset variable @restaurant_ratings = EXTRACT rest_id int, name string, tackle string, online_order bool, book_order bool, fee double, votes int, cellphone string, place string, rest_type string, favorite_dish_id int FROM "/Samples/information/restaurants_ratings.csv" the use of Extractors.Csv(); // No Transformation – extracted information is loaded as is to output file (TSV) OUTPUT @restaurant_ratings TO "/output/restaurants_out.tsv" the use of Outputters.Tsv();

    The scripts would write the complete restaurant rowsets to output file in a tab separated format.

    note that C# datatypes are used right here (e.g. string and never char/varchar as usually utilized in SQL). now not only will they use the datatypes of C#, however expressions and all of the goodness of an expressive programming language.

    U-SQL script with seriously change Step // Script - RestaurantScript.usql // Variables for enter and output file name and paths DECLARE @inputFile = "/Samples/information/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // statistics is extracted from enter file (CSV) and saved in employees rowset variable @restaurant_ratings = EXTRACT rest_id int, name string, handle string, online_order bool, book_order bool, fee double, votes int, cellphone string, location string, rest_type string, favorite_dish_id int FROM @inputFile the usage of Extractors.Csv(skipFirstNRows:1); // bypass first row which comprise headers // Transformation Step: Columns are renamed and no of rows are filtered @bestOnlineRestaurants = choose name.ToUpper() AS identify, // converting the names to uppercase fee AS score, online_order AS OnlineOrder, telephone AS phone, place AS location, rest_type AS category, favorite_dish_id AS FavoriteDishId FROM @restaurants_rating where rate > 4 && online_order == real; // Load transformed statistics to output dossierOUTPUT @bestOnlineRestaurants TO @outputFile the use of Outputters.Tsv(outputHeader:actual); // Write column names/headers to output file extend U-SQL expression using customized code

    U-SQL supports customized expressions written in C# code. The C# code resides in code at the back of files. note within the under diagram every .usql file has an linked .usql.cs file the place custom C# code resides. figure 8: U-SQL mission with distinctive script and code at the back of information

    // Code in the back of C# file - RestaurantScript.usql.cs namespace UsqlApp1 public static classification Helpers public static string FormatRestaurantName(string identify, string location, string restaurantType) return name + " (" + restaurantType + ") - " + area; // observe that U-SQL does not yet assist new C# 7.0 string interpolation // return $"identify ( restaurantType ) - location"; // Script - RestaurantScript.usql // Variables for input and output file name and paths DECLARE @inputFile = "/Samples/data/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // information is extracted from input file (CSV) and kept in personnel rowset variable @restaurant_ratings = EXTRACT rest_id int, identify string, handle string, online_order bool, book_order bool, rate double, votes int, cell string, location string, rest_type string, favorite_dish_id int FROM @inputFile the use of Extractors.Csv(skipFirstNRows:1); // skip first row which contain headers // Transformation Step: Columns are renamed and no of rows are filtered @bestOnlineRestaurants = opt for USQLApp1.Helpers.FormatRestaurantName(identify, place, rest_type) AS identify, rate AS ranking, online_order AS OnlineOrder, mobile AS cell, favorite_dish AS FavoriteDish FROM @restaurant_ratings the place expense > four && online_order == actual; // Load converted facts to output dossierOUTPUT @bestOnlineRestaurants TO @outputFile the use of Outputters.Tsv(outputHeader:real); // Write column names/headers to output file U-SQL script performing joins

    U-SQL helps joins between two distinct datasets. It offers internal join, Outer be a part of, cross join, and so forth.In here code snippet, they function inner join between a restaurants dataset and dish ingredients dataset.

    // Script - RestaurantScript.usql DECLARE @inputFile = "/Samples/statistics/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // data is extracted from input file (CSV) and kept in employees rowset variable @restaurant_ratings = // Code no longer proven for brevity. exact identical code as above illustration // Transformation Step: Columns are renamed and no of rows are filtered @bestOnlineRestaurants = // Code no longer shown for brevity. exact identical code as above example

    Now, they might want statistics about dishes and their parts. although this information customarily would be latest in an external supply, they might use an in-memory rowset right here.

    // Declare an in-reminiscence rowset for dish elements containing dish identification, identify of dish and // materials. @dish_ingredients = opt for * FROM (VALUES (1, "Biryani", "Rice, Indian spices, greens, Meat, Egg, Yoghurt, Dried Fruits"), (2, "Masala Dosa", "rice, husked black gram, mustard seeds, fenugreek seeds, salt, vegetable oil, potatoes, onion, green chillies, curry leaves, turmeric"), (three, "Cake", " sugar, butter, egg, cocoa, creme, salt") ) AS D(DishId, Dish, components); // function an inner be a part of between @bestOnlineRestaurants and @dish_ingredients rowset @rs_innerJn = choose r.identify, r.score, i.Dish, i.ingredientsFROM @bestOnlineRestaurants AS r inner join @dish_ingredients AS i ON r.FavoriteDishId == i.DishId; // Write to output dossierOUTPUT @rs_innerJn TO @outputFile the usage of Outputters.Tsv(outputHeader:genuine);

    This returns the restaurants with larger ratings, along with the ingredient particulars of its favorite dish, which is retrieved by way of becoming a member of the restaurant details rowset with dish ingredient rowset via inner be part of.

    [Click on the image to enlarge it]

    figure 9: U-SQL challenge with distinct script and code in the back of files

    U-SQL script the usage of built-in functions

    U-SQL provides a bunch of built-in functions together with combination services, analytical capabilities, ranking features and many others. under are few samples.

    type of feature     illustration combination functionsAVG, SUM, count number, STDEV (standard Deviation), MIN, MAX and so forth. Analytical applicationsFIRST_VALUE, LAST_VALUE, LAG, LEAD, PERCENTILE_CONT and so on. ranking purposesRANK, DENSE_RANK, NTILE, ROW_NUMBER and so forth.

    in the under script, we're using built-in mixture capabilities like MIN, MAX, AVG and STDEV on for eaterie.

    // Declare variables for enter and output files DECLARE @inputFile = "/Samples/facts/restaurants_raw_data.csv"; DECLARE @outputFile = "/output/restaurants_aggr.csv"; @restaurant_ratings = EXTRACT rest_id int, identify string, handle string, online_order bool, book_order bool, price double, votes int, phone string, area string, rest_type string, favorite_dish_id int FROM @inputFile the use of Extractors.Csv(skipFirstNRows:1); @output = opt for rest_type AS RestaurantType, MIN(fee) AS MinRating, MAX(fee) AS MaxRating, AVG(rate) AS AvgRating, STDEV(expense) AS StdDevRating FROM @restaurants_ratings group through rest_type; // Write to output fileOUTPUT @output TO @outputFile the use of Outputters.Csv(outputHeader:actual); U-SQL catalog

    so far, they now have focused on unstructured and semi-structured information being examine from information and written to info. while one among U-SQL’s strengths is to function on unstructured information saved in info and provide a schematized view on exact of unstructured data, it could actually control structured statistics. It gives a customary metadata catalog gadget like Hive. under is a list of fundamental objects supported by way of U-SQL:

  • Database: U-SQL helps databases similar to other big facts systems like Hive.
  • Database Schema: Database schemas neighborhood linked objects latest under a Database, precisely like relational databases.
  • Tables and Indexes: Tables are containers to cling structured statistics. Tables can include columns of distinct facts kinds. desk information is saved in data. Tables provide extra benefits above simply schematized views over unstructured info like indexing, partitioning desk records into distinctive buckets with each and every bucket backed up via a file.
  • Views: U-SQL views are of two kinds – (i) views which are according to a U-SQL table and (ii) views that point to a file and use EXTRACT to get the statistics.
  • features: supports each scalar and desk valued functions.
  • tactics:  techniques are comparable to features but they don’t return any value.
  • Assemblies: U-SQL supports storing .web assemblies which extends U-SQL scripts with custom expression.
  • Now, let’s say in their restaurant rating instance, they would want to further analyze restaurants with low ratings. To do so, they might want to movement the entire eating places with under a four score to a U-SQL desk for extra evaluation.

    U-SQL database, tables and indexes

    within the beneath instance. they are able to create a U-SQL database which may be created in the database with a schema and index key. They aren't making a schema mainly here, so the desk should be created below the default schema ‘dbo’ (bear in mind SQL Server?) internal the database.

    The beneath code example shows the way to create this desk.

    // Script - RestaurantScript.usql DECLARE @inputFile = "/Samples/records/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // statistics is extracted from enter file (CSV) and stored in employees rowset variable @restaurant_ratings = // Code no longer shown for brevity. real same code as above instance // Transformation Step: Filter handiest those restaurants with score under four @lowRatedRestaurants = choose rest_id AS RestaurantId, name AS name, rate AS score, online_order AS OnlineOrder, telephone AS mobilephone, area AS region, rest_type AS category, favorite_dish_id AS FavoriteDishId FROM @restaurants_ratings where price < 4; // Insert Low rated restaurant particulars to U-SQL Catalog // Create the database if it doesn't exist alreadyCREATE DATABASE IF no longer EXISTS RestaurantsDW; USE RestaurantsDW; // Drop the table if it exists DROP desk IF EXISTS dbo.LowRatedRestaurants; // Create the table via specifying the column schema and index CREATE desk dbo.LowRatedRestaurants( RestaurantId int, identify string, INDEX idx CLUSTERED (name DESC) allotted by way of HASH(name), score double, OnlineOrder bool, telephone string, place string, category string, FavoriteDishId int ); // Insert the rowset information to the U-SQL table created just before INSERT INTO dbo.LowRatedRestaurants select * FROM @lowRatedRestaurants; U-SQL views

    U-SQL views are similar to database views– they don't bodily keep the records and provide a view over records kept in table or information. Views may be in keeping with desk or according to an extraction over info.

    The instance script beneath shows how to create a view that’s in accordance with an extraction.

    USE DATABASE RestaurantsDW; // Delete the View if it already exists DROP VIEW IF EXISTS RestaurantsView; // Create the View based on an extraction CREATE VIEW RestaurantsView AS EXTRACT rest_id int, identify string, handle string, online_order bool, book_order bool, price double, votes int, mobilephone string, location string, rest_type string, favorite_dish_id int FROM "/Samples/facts/restaurants_raw_data.csv" the usage of Extractors.Csv(skipFirstNRows:1); // skip first row which include headers

    To run the view right here code is used:

    @result = select * FROM RestaurantsDW.dbo.RestaurantsView; OUTPUT @outcomes TO "/output/Restaurants_View.csv" the usage of Outputters.Csv(); U-SQL desk valued services (TVF)

    U-SQL supports each scalar characteristic and desk valued characteristic (TVF). services take zero to many arguments and return both a single scalar price or a desk, which is a dataset produced from columns and rows.

    The below code snippet indicates first how to create a TVF after which the way to invoke it. It takes a single parameter and returns a table.

    CREATE DATABASE IF no longer EXISTS RestaurantsDW; USE DATABASE RestaurantsDW; DROP feature IF EXISTS tvf_SearchRestaurants; // Create the desk Valued function that accepts Restaurant class as string // and returns a table that carries matched restaurant details. CREATE feature tvf_SearchRestaurants(@RestaurantType string) RETURNS @searchRestaurants desk(rest_id int, identify string, handle string, online_order bool, book_order bool, rate double, votes int, cell string, area string, rest_type string, favorite_dish_id int) AS start @allRestaurants = EXTRACT rest_id int, identify string, handle string, online_order bool, book_order bool, cost double, votes int, telephone string, location string, rest_type string, favorite_dish_id int FROM "/Samples/facts/restaurants_raw_data.csv" the usage of Extractors.Csv(skipFirstNRows:1); // skip first row which comprise headers @searchRestaurants = select * FROM @allRestaurants where rest_type == @RestaurantType; RETURN; end;

    Now let’s invoke the table valued function they just created and pass ‘Bakery’ as parameter– it would return all of the eating places which can be of category Bakery.

    OUTPUT RestaurantsDW.dbo.tvf_SearchRestaurants("Bakery") TO "/output/BakeryRestaurants.csv" the use of Outputters.Csv(); Case look at

    here case look at highlights using Azure statistics Lake Analytics and U-SQL language in a multiyear, gigantic, strategic digital transformation application. The customer, a huge assurance predominant, over the yr bought varied coverage agencies and brokers, and as a result used dissimilar client engagement techniques for interacting with valued clientele over electronic mail, textual content/SMS, web/cell chat and calls (each inbound and outbound). on account of the fractured strategy, it became very tricky for the client to analyze customer interplay facts.

    while the customer launched into a journey to build an omni channel platform and an built-in contact core for customer service over a lot of channels (e-mail, textual content, chat bot, contact center voice calls), their immediate tactical option become to investigate data from various sources for e-mail, text/SMS, chat and contact logs.

    An Azure statistics Lake-based mostly answer turned into developed to answer the immediate need of analyzing information from distinct techniques, in different codecs. information from a lot of source programs had been moved to Azure facts Lake shop and have been then analyzed using Azure information Lake evaluation and U-SQL.

  • Ingest – within the ingest part, unstructured and structured statistics from two distinctive sources (electronic mail/text/Chat information as well as call Log) are moved to Azure using Azure data manufacturing unit ETL carrier.
  • shop – raw statistics is stored on Azure information Lake Storage/ADLS as flat files.
  • Analyze – numerous forms of evaluation together with filtering, joins, aggregation, windowing and many others. are carried out in U-SQL.
  • model and Serve – Analyzed facts is saved in structured tables for later consumption from energy BI/custom reviews via user.
  • [Click on the image to enlarge it]

    figure 10: Azure data Analytics Pipeline


    Azure statistics Lake Storage and Analytics have emerged as a robust choice for performing massive facts and analytics workloads in parallel with Azure HDInsight and Azure Databricks. even though it’s nevertheless in its early days and lacks streaming and experience processing capabilities, its vigor lies within the new U-SQL language which combines the simplicity and ubiquity of SQL with Mirosoft’s flagship, the effective C# language. also, Microsoft’s construction tools like visible Studio and native dev/examine skill make it an impressive competitor in huge facts & analytics space.

    about the author

    Aniruddha Chakrabarti has 19 years of journey spread throughout approach, consulting, product development and IT capabilities. He has experience throughout services together with solution architecture, presales, know-how architecture, birth leadership and program administration. As AVP of digital in Mphasis, Chakrabarti is accountable for presales, answer, RFP/RFI and know-how architecture of colossal digital deals and courses. in advance of becoming a member of Mphasis he has performed quite a few leadership and architecture-concentrated roles at Accenture, Microsoft, goal, Misys and Cognizant. His center of attention areas encompass cloud, huge information & analytics, AI/ML, NLP, IoT, disbursed techniques, microservices and DevOps.

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