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    What is Azure Synapse Analytics?

    An Overview of Azure Synapse Analytics

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    What is Azure Synapse Analytics?

    Article 02/24/2022 3 minutes to read

    Azure Synapse is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems. Azure Synapse brings together the best of SQL technologies used in enterprise data warehousing, Spark technologies used for big data, Data Explorer for log and time series analytics, Pipelines for data integration and ETL/ELT, and deep integration with other Azure services such as Power BI, CosmosDB, and AzureML.

    Industry-leading SQL

    Synapse SQL is a distributed query system for T-SQL that enables data warehousing and data virtualization scenarios and extends T-SQL to address streaming and machine learning scenarios.

    Synapse SQL offers both serverless and dedicated resource models. For predictable performance and cost, create dedicated SQL pools to reserve processing power for data stored in SQL tables. For unplanned or bursty workloads, use the always-available, serverless SQL endpoint.

    Use built-in streaming capabilities to land data from cloud data sources into SQL tables

    Integrate AI with SQL by using machine learning models to score data using the T-SQL PREDICT function

    Industry-standard Apache Spark

    Apache Spark for Azure Synapse deeply and seamlessly integrates Apache Spark--the most popular open source big data engine used for data preparation, data engineering, ETL, and machine learning.

    ML models with SparkML algorithms and AzureML integration for Apache Spark 3.1 with built-in support for Linux Foundation Delta Lake.

    Simplified resource model that frees you from having to worry about managing clusters.

    Fast Spark start-up and aggressive autoscaling.

    Built-in support for .NET for Spark allowing you to reuse your C# expertise and existing .NET code within a Spark application.

    Working with your Data Lake

    Azure Synapse removes the traditional technology barriers between using SQL and Spark together. You can seamlessly mix and match based on your needs and expertise.

    Tables defined on files in the data lake are seamlessly consumed by either Spark or Hive.

    SQL and Spark can directly explore and analyze Parquet, CSV, TSV, and JSON files stored in the data lake.

    Fast, scalable data loading between SQL and Spark databases

    Built-in data integration

    Azure Synapse contains the same Data Integration engine and experiences as Azure Data Factory, allowing you to create rich at-scale ETL pipelines without leaving Azure Synapse Analytics.

    Ingest data from 90+ data sources

    Code-Free ETL with Data flow activities

    Orchestrate notebooks, Spark jobs, stored procedures, SQL scripts, and more

    Data Explorer (Preview)

    Azure Synapse Data Explorer provides customers with an interactive query experience to unlock insights from log and telemetry data. To complement existing SQL and Apache Spark analytics runtime engines, Data Explorer analytics runtime is optimized for efficient log analytics using powerful indexing technology to automatically index free-text and semi-structured data commonly found in the telemetry data.

    Use Data Explorer as a data platform for building near real-time log analytics and IoT analytics solutions to:

    Consolidate and correlate your logs and events data across on-premises, cloud, third-party data sources.

    Accelerate your AI Ops journey (pattern recognition, anomaly detection, forecasting, and more)

    Replace infrastructure-based log search solutions to save cost and increase productivity.

    Build IoT Analytics solution for your IoT data.

    Build Analytical SaaS solutions to offer services to your internal and external customers.

    Unified experience

    Synapse Studio provides a single way for enterprises to build solutions, maintain, and secure all in a single user experience

    Perform key tasks: ingest, explore, prepare, orchestrate, visualize

    Monitor resources, usage, and users across SQL, Spark, and Data Explorer

    Use Role-based access control to simplify access to analytics resources

    Write SQL, Spark or KQL code and integrate with enterprise CI/CD processes

    Engage with the Synapse community

    Microsoft Q&A: Ask technical questions.

    Stack Overflow: Ask development questions.

    Next steps

    Get started with Azure Synapse Analytics

    Create a workspace

    Use serverless SQL pool

    Create a Data Explorer pool using Synapse Studio (Preview)

    Recommended content

    Synapse SQL architecture - Azure Synapse Analytics

    स्रोत : learn.microsoft.com

    Understanding Azure Synapse Analytics

    Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing and big data analytics.

    Understanding Azure Synapse Analytics

    What is Azure Synapse Analytics?

    Image Source : Microsoft AzureData Warehouse is the central repository of integrated data from one or more disparate data sources aggregated as per business needs which is later used for reporting and data analysis. This is one of the core components of Business Intelligence.Big Data analytics is a process to extract meaningful insights, hidden patterns, unknown correlations, market trends from data sets that are too large or complex to be dealt with by traditional data-processing application software.Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing and big data analytics.

    Azure Synapse Analytics facilitates to query data on your terms, using either serverless or dedicated resources—at scale. Azure Synapse brings these worlds together with a unified experience to ingest, explore, prepare, transform, manage and serve data for immediate Business Intelligence and machine learning needs.

    Key components of Azure Synapse:

    Azure Synapse Analytics being an enterprise analytics service offered by Microsoft, consists of various key components like  –

    the best of SQLtechnologies used in enterprise data warehousing,

    Sparktechnologies used for big data,Data Explorerfor log and time series analytics,Pipelinesfor data integration and ETL / ELT,

    Deep integration with other Azure services such as

    Power BICosmosDB, and AzureML.

    Industry-leading SQL

    Synapse SQL is a distributed query system for T-SQL that enables data warehousing and data virtualization scenarios and extends T-SQL to address streaming and machine learning scenarios.

    Unlike an ordinary SQL Server database engine, Azure Synapse Analytics can receive data from a wide variety of sources. To do this, Azure Synapse Analytics uses a technology named PolyBase. PolyBase enables you to retrieve data from relational and non-relational Data sources DB without separately installing client connection software.

    Industry-standard Apache Spark

    Apache Spark for Azure Synapse deeply and seamlessly integrates Apache Spark–the most popular open source big data engine used for data preparation, data engineering, ETL, and machine learning.

    Working with your Data Lake

    Azure Synapse removes the traditional technology barriers between using SQL and Spark together. You can seamlessly mix, and match based on your needs and expertise.

    Built-in data integration

    Azure Synapse contains the same Data Integration engine and experiences as Azure Data Factory, allowing you to create rich at-scale ETL pipelines without leaving Azure Synapse Analytics.

    Data Explorer

    Azure Synapse Data Explorer provides customers with an interactive query experience to unlock insights from log and telemetry data. To complement existing SQL and Apache Spark analytics runtime engines, Data Explorer analytics runtime is optimized for efficient log analytics using powerful indexing technology to automatically index free-text and semi-structured data commonly found in the telemetry data.

    Unified experience

    Synapse Studio provides a single way for enterprises to build solutions, maintain, and secure all in a single user experience

    Perform key tasks: ingest, explore, prepare, orchestrate, visualize

    Monitor resources, usage, and users across SQL, Spark, and Data Explorer

    Use Role-based access control to simplify access to analytics resources

    Write SQL, Spark or KQL code and integrate with enterprise CI/CD processes

    Why Azure Synapse

    Image Source – Microsoft Azure

    Azure Synapse is used for Run analytics at a massive scale by using a cloud-based enterprise data warehouse that takes advantage of massively parallel processing to run complex queries quickly across petabytes of data.

    Traditional data warehouses and reports can’t scale to provide the intelligence and insight that business executives demand in today’s world. To make good strategic decisions, businesses need to find new insights quickly and effectively in their data. This can only come through more advanced tools and an improved understanding of how to get the most from them.

    Azure Synapse has an intelligent architecture that makes it industry-leading in unifying big data workloads with traditional data warehousing.

    स्रोत : intellifysolutions.com

    DP

    Study with Quizlet and memorize flashcards containing terms like What three main types of workload can be found in a typical modern data warehouse?, A ____________________ is a continuous flow of information, where continuous does not necessarily mean regular or constant., __________________________ focuses on moving and transforming data at rest. and more.

    DP-900

    4.8 (4 reviews) Term 1 / 204

    What three main types of workload can be found in a typical modern data warehouse?

    Click the card to flip 👆

    Definition 1 / 204 - Streaming Data - Batch Data - Relational Data

    Click the card to flip 👆

    Created by mcconnelljh

    Terms in this set (204)

    What three main types of workload can be found in a typical modern data warehouse?

    - Streaming Data - Batch Data - Relational Data

    A ____________________ is a continuous flow of information, where continuous does not necessarily mean regular or constant.

    data stream

    __________________________ focuses on moving and transforming data at rest.

    Batch processing

    This data is usually well organized and easy to understand. Data stored in relational databases is an example, where table rows and columns represent entities and their attributes.

    Structured Data

    This data usually does not come from relational stores, since even if it could have some sort of internal organization, it is not mandatory. Good examples are XML and JSON files.

    Semi-structured Data

    Data with no explicit data model falls in this category. Good examples include binary file formats (such as PDF, Word, MP3, and MP4), emails, and tweets.

    Unstructured Data

    What type of analysis answers the question "What happened?"

    Descriptive Analysis

    What type of analysis answers the question "Why did it happen?"

    Diagnostic Analysis

    What type of analysis answers the question "What will happen?"

    Predictive Analysis

    What type of analysis answers the question "How can we make it happen?"

    Prescriptive Analysis

    The two main kinds of workloads are ______________ and _________________.

    extract-transform-load (ETL)

    extract-load-transform (ELT)

    ______ is a traditional approach and has established best practices. It is more commonly found in on-premises environments since it was around before cloud platforms. It is a process that involves a lot o data movement, which is something you want to avoid on the cloud if possible due to its resource-intensive nature.

    ETL

    ________ seems similar to ETL at first glance but is better suited to big data scenarios since it leverages the scalability and flexibility of MPP engines like Azure Synapse Analytics, Azure Databricks, or Azure HDInsight.

    ELT

    _______________ is a cloud service that lets you implement, manage, and monitor a cluster for Hadoop, Spark, HBase, Kafka, Store, Hive LLAP, and ML Service in an easy and effective way.

    Azure HDInsight

    _____________ is a cloud service from the creators of Apache Spark, combined with a great integration with the Azure platform.

    Azure Databricks

    ____________ is the new name for Azure SQL Data Warehouse, but it extends it in many ways. It aims to be the comprehensive analytics platform, from data ingestion to presentation, bringing together one-click data exploration, robust pipelines, enterprise-grade database service, and report authoring.

    Azure Synapse Analytics

    A ___________ displays attribute members on rows and measures on columns. A simple ____________ is generally easy for users to understand, but it can quickly become difficult to read as the number of rows and columns increases.

    table

    A _____________ is a more sophisticated table. It allows for attributes also on columns and can auto-calculate subtotals.

    matrix

    Objects in which things about data should be captured and stored are called: ____________.

    A. tables B. entities C. rows D. columns B. entities

    You need to process data that is generated continuously and near real-time responses are required. You should use _________.

    A. batch processing

    B. scheduled data processing

    C. buffering and processing

    D. streaming data processing

    D. streaming data processing

    A. Extract, Transform, Load (ETL)

    B. Extract, Load, Transform (ELT)

    1. Optimize data privacy.

    2. Provide support for Azure Data Lake.

    1 - A 2 - B

    Extract, Transform, Load (ETL) is the correct approach when you need to filter sensitive data before loading the data into an analytical model. It is suitable for simple data models that do not require Azure Data Lake support. Extract, Load, Transform (ELT) is the correct approach because it supports Azure Data Lake as the data store and manages large volumes of data.

    The technique that provides recommended actions that you should take to achieve a goal or target is called _____________ analytics.

    A. descriptive B. diagnostic C. predictive D. prescriptive D. prescriptive A. Tables B. Indexes C. Views D. Keys

    1. Create relationships.

    2. Improve processing speed for data searches.

    3. Store instances of entities as rows.

    4. Display data from predefined queries.

    1 - D 2 - B 3 - A 4 - C

    The process of splitting an entity into more than one table to reduce data redundancy is called: _____________.

    A. deduplication B. denormalization C. normalization D. optimization C. normalization

    Azure SQL Database is an example of ________________ -as-a-service.

    A. platform B. infrastructure

    स्रोत : quizlet.com

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