This document provides an overview of Modern Analytics Academy and Azure Synapse Analytics. It introduces the Modern Analytics Academy team and their agenda to discuss modeling, data lakes, Synapse, and a demo. It then covers key concepts like the data lake, logical data warehouse, and data warehouse. It describes the role of data in modern analytics between data lakes and data warehouses. Finally, it introduces Azure Synapse Analytics and its capabilities for dedicated SQL pools, serverless SQL pools, and Apache Spark pools for unified analytics.
Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)Trivadis
In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
Prague data management meetup 2018-03-27Martin Bém
This document discusses different data types and data models. It begins by describing unstructured, semi-structured, and structured data. It then discusses relational and non-relational data models. The document notes that big data can include any of these data types and models. It provides an overview of Microsoft's data management and analytics platform and tools for working with structured, semi-structured, and unstructured data at varying scales. These include offerings like SQL Server, Azure SQL Database, Azure Data Lake Store, Azure Data Lake Analytics, HDInsight and Azure Data Warehouse.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
These are the slides for my talk "An intro to Azure Data Lake" at Azure Lowlands 2019. The session was held on Friday January 25th from 14:20 - 15:05 in room Santander.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
DocumentDB is a powerful NoSQL solution. It provides elastic scale, high performance, global distribution, a flexible data model, and is fully managed. If you are looking for a scaled OLTP solution that is too much for SQL Server to handle (i.e. millions of transactions per second) and/or will be using JSON documents, DocumentDB is the answer.
What is in a modern BI architecture? In this presentation, we explore PaaS, Azure Active Directory and Storage options including SQL Database and SQL Datawarehouse.
Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad. Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad. Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad. Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad.
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In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
Prague data management meetup 2018-03-27Martin Bém
This document discusses different data types and data models. It begins by describing unstructured, semi-structured, and structured data. It then discusses relational and non-relational data models. The document notes that big data can include any of these data types and models. It provides an overview of Microsoft's data management and analytics platform and tools for working with structured, semi-structured, and unstructured data at varying scales. These include offerings like SQL Server, Azure SQL Database, Azure Data Lake Store, Azure Data Lake Analytics, HDInsight and Azure Data Warehouse.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
These are the slides for my talk "An intro to Azure Data Lake" at Azure Lowlands 2019. The session was held on Friday January 25th from 14:20 - 15:05 in room Santander.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
DocumentDB is a powerful NoSQL solution. It provides elastic scale, high performance, global distribution, a flexible data model, and is fully managed. If you are looking for a scaled OLTP solution that is too much for SQL Server to handle (i.e. millions of transactions per second) and/or will be using JSON documents, DocumentDB is the answer.
What is in a modern BI architecture? In this presentation, we explore PaaS, Azure Active Directory and Storage options including SQL Database and SQL Datawarehouse.
Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad. Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad. Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad. Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad.
This document provides an overview of a course on implementing a modern data platform architecture using Azure services. The course objectives are to understand cloud and big data concepts, the role of Azure data services in a modern data platform, and how to implement a reference architecture using Azure data services. The course will provide an ARM template for a data platform solution that can address most data challenges.
Modern DW Architecture
- The document discusses modern data warehouse architectures using Azure cloud services like Azure Data Lake, Azure Databricks, and Azure Synapse. It covers storage options like ADLS Gen 1 and Gen 2 and data processing tools like Databricks and Synapse. It highlights how to optimize architectures for cost and performance using features like auto-scaling, shutdown, and lifecycle management policies. Finally, it provides a demo of a sample end-to-end data pipeline.
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
This document discusses building a data lake on AWS. It describes using Amazon S3 for storage, Amazon Kinesis for streaming data, and AWS Lambda to populate metadata indexes in DynamoDB and search indexes. It covers using IAM for access control, AWS STS for temporary credentials, and API Gateway and Elastic Beanstalk for interfaces. The data lake provides a foundation for storing and analyzing structured, semi-structured, and unstructured data at scale from various sources in a cost-effective and secure manner.
Sql Bits 2020 - Designing Performant and Scalable Data Lakes using Azure Data...Rukmani Gopalan
Cloud Storage is evolving rapidly, and our Azure Storage portfolio has added a ton of new industry leading capabilities. In this session you will learn the do's and don'ts of building data lakes on Azure Data Lake Storage. You will learn about the commonly used patterns, how to set up your accounts and pipelines to maximize performance, how to organize your data and various options to secure access to your data. We will also cover customer use cases and highlight planned enhancements and upcoming features.
AWS March 2016 Webinar Series Building Your Data Lake on AWS Amazon Web Services
Uncovering new, valuable insights from big data requires organizations to collect, store, and analyze increasing volumes of data from multiple, often disparate sources at disparate points in time. This makes it difficult to handle big data with data warehouses or relational database management systems alone.
A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema, enabling a far more agile and flexible architecture, which makes it easier to gain new types of analytical insights from your data
In this webinar, we will introduce key concepts of a Data Lake and present aspects related to its implementation. We will discuss critical success factors, pitfalls to avoid as well as operational aspects such as security, governance, search, indexing and metadata management.
Learning Objectives:
• Learn how AWS can help enable a Data Lake architecture
• Understand some of the key architectural considerations when building a Data Lake
• Hear some of the important Data Lake implementation considerations
Who Should Attend:
• Data architects, data scientists, advanced AWS developers
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
This document provides an overview of Azure Synapse Analytics and its key capabilities. Azure Synapse Analytics is a limitless analytics service that brings together enterprise data warehousing and big data analytics. It allows querying data on-demand or at scale using serverless or provisioned resources. The document outlines Synapse's integrated data platform capabilities for business intelligence, artificial intelligence and continuous intelligence. It also describes the different types of analytics workloads that Synapse supports and key architectural components like the dedicated SQL pool and massively parallel processing concepts.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Running cost effective big data workloads with Azure Synapse and ADLS (MS Ign...Michael Rys
Presentation by James Baker and myself on Running cost effective big data workloads with Azure Synapse and Azure Datalake Storage (ADLS) at Microsoft Ignite 2020. Covers Modern Data warehouse architecture supported by Azure Synapse, integration benefits with ADLS and some features that reduce cost such as Query Acceleration, integration of Spark and SQL processing with integrated meta data and .NET For Apache Spark support.
Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad.
Azure Data Engineering Course in Hyderabadsowmyavibhin
Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad.
"Azure Data Engineering Course in Hyderabad "madhupriya3zen
Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad.
SQL or NoSQL, is this the question? - George GrammatikosGeorge Grammatikos
This document provides an overview and comparison of SQL and NoSQL databases. It lists the most popular databases according to a Stack Overflow survey, including SQL databases like Azure SQL and NoSQL databases like Azure Cosmos DB. It then defines RDBMS and NoSQL databases and provides examples of relational and non-relational data models. The document compares features of SQL and NoSQL databases such as scalability, performance, data modeling flexibility and pricing. It also includes live demo instructions for provisioning Azure SQL and Cosmos DB databases.
Dustin Vannoy is a field data engineer at Databricks and co-founder of Data Engineering San Diego. He specializes in Azure, AWS, Spark, Kafka, Python, data lakes, cloud analytics, and streaming. The document provides an overview of various Azure data and analytics services including Azure SQL DB, Cosmos DB, Blob Storage, Data Lake Storage Gen 2, Databricks, Synapse Analytics, Data Factory, Event Hubs, Stream Analytics, and Machine Learning. It also includes a reference architecture and recommends Microsoft Learn paths and community resources for learning.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Data Analytics Meetup: Introduction to Azure Data Lake Storage CCG
Microsoft Azure Data Lake Storage is designed to enable operational and exploratory analytics through a hyper-scale repository. Journey through Azure Data Lake Storage Gen1 with Microsoft Data Platform Specialist, Audrey Hammonds. In this video she explains the fundamentals to Gen 1 and Gen 2, walks us through how to provision a Data Lake, and gives tips to avoid turning your Data Lake into a swamp.
Learn more about Data Lakes with our blog - Data Lakes: Data Agility is Here Now https://bit.ly/2NUX1H6
Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad.
Easily Verify Compliance and Security with Binance KYCAny kyc Account
Use our simple KYC verification guide to make sure your Binance account is safe and compliant. Discover the fundamentals, appreciate the significance of KYC, and trade on one of the biggest cryptocurrency exchanges with confidence.
How to Implement a Strategy: Transform Your Strategy with BSC Designer's Comp...Aleksey Savkin
The Strategy Implementation System offers a structured approach to translating stakeholder needs into actionable strategies using high-level and low-level scorecards. It involves stakeholder analysis, strategy decomposition, adoption of strategic frameworks like Balanced Scorecard or OKR, and alignment of goals, initiatives, and KPIs.
Key Components:
- Stakeholder Analysis
- Strategy Decomposition
- Adoption of Business Frameworks
- Goal Setting
- Initiatives and Action Plans
- KPIs and Performance Metrics
- Learning and Adaptation
- Alignment and Cascading of Scorecards
Benefits:
- Systematic strategy formulation and execution.
- Framework flexibility and automation.
- Enhanced alignment and strategic focus across the organization.
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This document provides an overview of a course on implementing a modern data platform architecture using Azure services. The course objectives are to understand cloud and big data concepts, the role of Azure data services in a modern data platform, and how to implement a reference architecture using Azure data services. The course will provide an ARM template for a data platform solution that can address most data challenges.
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Uncovering new, valuable insights from big data requires organizations to collect, store, and analyze increasing volumes of data from multiple, often disparate sources at disparate points in time. This makes it difficult to handle big data with data warehouses or relational database management systems alone.
A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema, enabling a far more agile and flexible architecture, which makes it easier to gain new types of analytical insights from your data
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This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
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2. Agenda • What is Modern Analytics Academy?
• The Team
• Modern Analytics in Azure
Model & Serve
Data Lake Structure
Synapse as solution
Demo
Review
3. Senior Cloud Solution
Architect
Alex Karasek
Modern Analytics Academy Team
Principal Cloud Solution
Architect
Chris Mitchell
Principal Cloud Solution
Architect
Jason Virtue
Senior Cloud Solution
Architect
Annie Xu
Senior Cloud Solution
Architect
Brian Hitney
https://aka.ms/maa
4. Academy Sessions
Acquisition &
Storage
Modeling Pipelines Security &
Governance
Visualization
Azure Data Factory
Synapse Pipelines
Power BI Dataflows
Azure Stream
Analytics
Data Lake Structure
Synapse Spark Pools
Synapse SQL Pools
Synapse Serverless
SQL
Azure Data Lake
Azure Cosmos DB
Azure Event Hubs
Synapse Link
Auditing
Security
Azure purview
Power BI
Paginated Reports
Power BI Embedded
5. Modern Analytics in Azure
Advanced Analytics
INGEST PREP & TRAIN MODEL & SERVE BI + Reporting
Real Time Analytics
STORE
Big data store
EXPLORE
Query All Data Analytics Engines Data Warehouse
Data Orchestration
and Monitoring
METADATA MANAGEMENT & GOVERNANCE
Social
LOB
Graph
IoT
Image
CRM
6. Role of Data in Modern Analytics
Experimentation
Fast exploration
Semi-structured data
Big Data
OR
Proven security & privacy
Dependable performance
Operational data
Relational Data
Data Lake Data Warehouse
7. Role of Data in Modern Analytics
Data warehousing & big data analytics—all in one service
Azure Synapse Analytics
Data warehousing & big data analytics—all in one service
Azure Synapse Analytics
8. Introducing Azure
Synapse Analytics
A limitless analytics service with unmatched
time to insight, that delivers insights from all
your data, across data warehouses and big
data analytics systems, with blazing speed
Simply put, Azure Synapse is Azure SQL Data
Warehouse evolved
We have taken the same industry leading data
warehouse and elevated it to a whole new level of
performance and capabilities
10. Key Terms
Data Lake
A data lake is a storage repository that holds a large amount of data in its native, raw format. Data lake
stores are optimized for scaling to terabytes and petabytes of data. The data typically comes from
multiple heterogeneous sources, and may be structured, semi-structured, or unstructured. The idea with
a data lake is to store everything in its original, untransformed state. This approach differs from a
traditional data warehouse, which transforms and processes the data at the time of ingestion.
Azure Data Lake (ADLS Gen 2)
Data Lake Storage Gen2 makes Azure Storage the foundation for building enterprise data lakes on
Azure
Logical Data Warehouse (LDW)
A relational layer built on top of Azure data sources such as Azure Data Lake storage (ADLS Gen 2),
Azure Cosmos DB analytical storage, or Azure Blob storage
Data Warehouse (DW)
A data warehouse is a centralized repository of integrated data from one or more disparate sources.
Data warehouses store current and historical data and are used for reporting and analysis of the data.
12. Azure Synapse Analytics
Rich surface area
T-SQL language for data analytics
Supporting large number of
languages and tools
Enterprise-grade security
dedicated SQL pool
Modern Data Warehouse
Indexing and caching
Import and query external data
Workload management
serverless SQL pool
Querying external data
Model raw files as virtual tables
and views
Easy data transformation
13. Azure Synapse Analytics
Dedicated SQL Pools (Formerly SQL DW)
Best in class price
per performance
Developer
productivity
Intelligent workload
management
Data flexibility
Up to 94% less expensive
than competitors
Prioritize resources for
the most valuable
workloads
Ingest variety of data
sources to derive the
maximum benefit
Use preferred tooling for
SQL data warehouse
development
Industry-leading
security
Defense-in-depth
security and 99.9%
financially backed
availability SLA
15. Azure Synapse Analytics
Serverless SQL Pools
Quick data exploration
Easily explore schema and data in
files on Azure storage
Supports various file formats
(Parquet, CSV, JSON)
Direct connector to Azure storage
for large BI ecosystem
Logical Data Warehouse
Model raw files as virtual tables and
views
Use any tool that works with SQL to
analyze files
Use enterprise-grade security model
Easy data transformation
Transform CSV to parquet format
Move data between containers and
accounts
Save the results of queries on
external storage
16. Azure Synapse Analytics
Apache Spark Pools
Spark Unifies:
Batch Processing
An unified, open source, parallel, data processing framework for Big Data Analytics
Spark Core Engine
Spark SQL
Batch processing
Spark MLlib
Machine
Learning
Yarn
Spark MLlib
Machine
Learning
Spark
Streaming
Stream processing
GraphX
Graph
Computation
The agenda for today’s session will include:
A review of the modern analytics academy framework
The team behind it
An overview of a modern analytics architecture in Azure
Finally a deep dive into ways to model and serve data in Azure. Specific topics covered will include data lake structures for analytical use cases, how synapse fits as a solution and a demo of these concepts
The MAA was built to show breadth and depth of capabilities in Azure that can be used to implement a Modern Analytics Solution in the cloud. This is the team behind this program, and we are all Cloud Solution Architectes on Microsoft’s Global Partner Solutions Team.
Let’s take a quick look at the detailed sessions available.
In Data Acquisition and Storage we looked at the nuances of how source systems provide data and the various mechanisms for getting that data into Azure for long term retention and analysis.
In Data Modeling we’ll be looking at how to structure and store your data for the purposes of serving data to applications like reporting tools.
In Data Pipelines we’ll be looking at how to move your data through a data engineering process and prepare the data for serving.
In Data Security and Governance we’ll examine how to deal with topics like controlling access to your data, understanding the lineage of your data and addressing policy compliance on your data.
In Data Visualization we’ll examine how visualization tools like Power BI can be used to enable users to get timely business value out of your data.
Ingestion – we need to be able to connect to the data no matter where it comes from. Line of business applications, cloud services, social networks, sensor networks as well as being able to support the different cadences with which that data arrives.
Storage - highly scalable and cost effect storage is critical to these solutions, with theoretically limitless amounts of data being required to support these types of solutions, choosing the right methods for storage is always critical
Exploration – data processing engines and services are used more for ad hoc data exploration of data
Prep & Train - New analytics and data processing engines such as Spark are being leveraged to prepare data and train models over large data volumes
Model & Serve – Without giving users access to the data to do ad-hoc analysis an analytics solution is useless. We must surface the data in a useable way through modern tools.
Metadata management & governance – with all this data security and regulatory concerns haven’t gone away, data must be discoverable, compliant, and trusted.
Data is fundamental to the types of modern analytics solutions that we are discussing in this series. When it comes to data at this scale there are generally 2 ways to store, model, and serve to consumes. Data Lakes are generally ideal for experimentation, exploratory, or ad hoc queries over semi-structured data at any scale. For more traditional analytics and reporting scenarios, a DW is often more suitable for serving data via a relational data model with proven security and dependable performance over aggregated operational data sets.
By being able to support both scenarios, Synapse Analytics becomes the natural hub for all of your modern analytical solutions
Designed for analytics workloads at any scale
Interfaces:
Synapse Studio – web based portal for object exploration, data engineering, development, orchestration all through a single pane of glass
SaaS developer experiences for code free and code first
Multiple languages suited to different analytics workloads
Integrated analytics runtimes available provisioned and serverless on-demand
SQL Analytics offering T-SQL for batch, streaming and interactive processing
Spark for big data processing with Python, Scala, R and .NET
Integrated platform services for, management, security, monitoring, and metastore
Data lake integrated and Common Data Model aware
Raw data: This is data as it comes from the source systems. This data is typically ingested in raw format via automated streaming or batch oriented pipelines and is consumed by an analytics engine such as Spark or managed integration pipelines in ADF or Synapse to perform cleansing and enrichment operations to generate the enriched and curated data. Common formats might include csv or JSON which are optimized for efficient write operations
Enriched data: This layer of data is the version where raw data (as is or aggregated) has a defined schema and also, the data is cleansed, enriched (with other sources) and is available to analytics engines to extract high value data. Data engineers generate these datasets and also proceed to extract high value/curated data from these datasets. Data in this zone might be stored in a format such as parquet which is compressed and includes schema which makes it much more suitable for read heavy and analytical workloads.
Curated data: This layer of data contains the high value information that is served to the consumers of the data – the BI analysts and the data scientists. This data has structure and can be served to the consumers either as is in its native format or through a more traditional data warehouse. Data assets in this layer is usually highly governed and well documented. Data in this zone might be stored in a format like delta which is an enhanced version of parquet that leverages a transaction log to enable advanced capabilities like merge operations, time travel, and significantly better performance when using for analytical queries
An interactive query service that enables you to use standard T-SQL queries over files in Azure storage.
Having Spark integrated directly inside of Synapse allows users to pick the appropriate query engine for every scenario without ever needing to leave the Synapse workspace environment. Because the available Hive metastore is automatically synced with SQL Serverless engine, data can be directly integrated across platforms, and because it is embedded directly inside of Synapse, that means that all of the associated benefits such as security, administration, monitoring, etc… would apply. Some supported use cases that are made easier through the use of this embedded engine include:
Batch Processing
Interactive SQL
Real-time processing
Machine Learning
Deep Learning
Graph Processing