Processing Unstructured Data. Dinesh Priyankara Founder/Principal Architect dinesql Pvt Ltd.

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1 Processing Unstructured Data Dinesh Priyankara Founder/Principal Architect dinesql Pvt Ltd. /

2 Dinesh Founder/Principal Architect dinesql Pvt Ltd. Microsoft Most Valuable Professional Since 2006, Data Platform (SQL Server) Consultant, Trainer, Speaker MSc in IT, MCSE, MCDBA

3 Agenda 01 Understanding unstructured data 02 Introduction to Hadoop and MapReduce 03 The Microsoft way 04 Demo 05 Q & A

4 01 Understanding unstructured data

5 Structured Data Structured data resides in a fixed field within a record or file. Relational databases and spreadsheets hold structured data Always integrated with a schema (model) Schema defines the structure of data with data types such as string, integers, date, etc. Schema defines how data is stored, accessed and processed. Easy maintainability and data management Based on Schema-on-write method Managed with most known Structured Query Language (SQL)

6 Semi-Structured Data Semi-Structured data does not follow a standard model defined with a schema. Structure is imposed in the form of tags or markers. Different set of attributes in elements even though they are belong to one class. Example: XML, HTML, JSON, etc. Considered as self-describing data.

7 Unstructured Data: The Definition Unstructured Data (or unstructured information) refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner ~ wikipedia Unstructured data represents any data that does not have a recognizable structure. It is unorganized and raw and can be nontextual or textual ~ techopedia Structured data is applied to a database we know while unstructured data applies to everything else ~ web

8 Unstructured Data Unstructured data does not reside in a field or record No standard model, does not follow a schema. No specific definition on storing, accessing and processing. Can be seen as word documents, audio files, videos, photos, etc. Might follow a structure internally But no schema, tags, or markers describing the fields of data. Difficult to process using traditional computer modules. Has many irregularities and ambiguities

9 Why it is important? 80%-90% data is unstructured and it grows Previously unidentified or ignored. Hidden business insight Provides holistic view of the business Provides competitive advantages Reveals social trends for improving customer satisfaction Saves time and money 2.5 quintillion bytes of data per day 175 million tweets per day 1.49 billion monthly active FB users

10 Common Unstructured and Semi-structured Data Sentiment data mainly from social networks, online reviews, customer support interaction Clickstream data Sensor or machine data Server log data

11 Ways of accessing Unstructured Data Mainly two methods Impose a structure on unstructured data Based on schema-on-read method Transform unstructured data into a structured schema Permanent structure makes it accessible by traditional computer modules.

12 02 Introduction to Hadoop and MapReduce

13 Hadoop Name Node Hadoop Cluster Data Nodes HDFS It is a scalable, fault tolerant, open source framework for the distributed storing and processing of large sets of data on commodity hardware. Mainly used for; Converting unstructured data to structured format (ETL/ELT) This is what we primarily focused Analysing data efficiently Using as a data archive

14 MapReduce Date Country Quantity 1/1/2015 Sri Lanka 10 1/1/2015 India 15 1/1/2015 India 5 1/2/2015 Australia 5 1/2/2015 Sri Lanka 20 Date Country Quantity 1/2/2015 Sri Lanka 5 1/3/2015 Australia 5 1/3/2015 Australia 10 1/3/2015 USA 15 1/4/2015 USA 25 Date Country Quantity 1/4/2015 USA 10 1/4/2015 Sri Lanka 25 1/4/2015 India 5 1/5/2015 India 5 1/5/2015 India 5 Equivalent to: SELECT Country, SUM(Quantity) FROM Sales GROUP BY Country 1. Map phase generates key/value pairs Key Sri Lanka 10 India 15 India 5 Value Australia 5 Sri Lanka 20 Key Sri Lanka 5 Australia 5 Australia 10 USA 15 USA 25 Value 2. Reduce phase aggregates values for each key Key Sri Lanka 60 India 35 USA 50 Australia 20 Value Key Value USA 10 Is best suited for batch processing Used for Data analytics Data mining Full-text indexing Sri Lanka 25 India 5 India 5 India 5

15 Hadoop supportive project: Hive Originally developed by Facebook as a supporting project for the main Apache Hadoop project Is a data warehouse infrastructure built on Hadoop Is an abstraction layer on top of MapReduce Provides Hive Query Language (HiveQL) SQL-Like language and interfaces Schema-On-Read Converts queries to MapReduce jobs (nof reducers are automatically decided)

16 03 The Microsoft Way

17 The Microsoft way A platform for process, discover and explore any data no matter the size, type, or location inside the walls of your business, in public data sources, or on the web. Microsoft product suite includes; SQL Server product suite Analytical Platform System (APS) (MPP+PDW+HDInsight) Integration Services + Azure Services SQL Server PolyBase Azure Cloud service Azure SQL Data Warehouse Azure Data Lake Azure Data Lake Store Azure Data Lake Analytics Azure HDInsight. Azure Data Factory

18 Azure HDInsight Cloud-based implementation of Hadoop ecosystem Support open source projects like Pig, Hive, Spark, HBase, etc., Third party applications like DataMeer, Blue Talon or Concurrent Available as a Windows Azure service Architecture includes; One or more virtual machines Windows Azure Storage - Blob storage for HDFS Windows Azure SQL Database Metadata store for Hive and Oozie Windows Azure HDInsight cluster (VMs) HDFS Blob Store container(s ) Hive/Oozie Metadata SQL Database

19 Microsoft Hive ODBC Driver ETL Unstructured data DBs/DWs for reporting

20 Processing Unstructured data with SSIS Microsoft Hive ODBC Driver HiveQL Azure Image taken from MSDN

21 SSIS Feature Pack for Azure Extend the workflow to use Azure storage, HDInsight, Machine learning, etc. On-Demand cluster Azure Services Unstructured data ETL DBs/DWs for reporting

22 Processing Unstructured data with SSIS Feature Pack for Azure Image taken from MSDN

23 DEMO Processing unstructured data with Integration Services + Azure Feature Pack + HDInsight + Hive Drivers

24 PolyBase A SQL Server facilitator for Azure and Hadoop Services Requests Output PolyBase Hadoop Azure Blob Storage Image taken from MSDN

25 DEMO Getting semi-structured data using PolyBase

26 04 Q & A

27 References Hive ODBC Driver Azure Feature Pack for SSIS Azure HDInsight

28 Thank You

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