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Building A Scalable Data Warehouse With Data Vault 2.0 Downloads Torrent
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The Data Vault 2.0 System Of Business Intelligence represents a major evolution of the already successful Data Vault architecture. It has been extended beyond the Data Warehouse component to include a model capable of dealing with cross-platform data persistence, multi-latency and multi-structured data and massively parallel platforms. It also includes an agile methodology called Disciplined Agile Deliveries (DAD) within it and is both automation and load friendly.
Using the Data Cloud as a data hub, you effectively just eliminate all barriers. You can speed up innovation, you can speed up data use, you can speed up the improvement of our relationships with both our customers and our B2B partners. And that's kind of what it's all about.
There exists plenty of literature on the web about Data Vault and often the message and methodology is conflicting and designed with old school principles to data delivery or even focussed on a particular understanding of agile delivery. As a data professional with experience in ETL/ELT, data architectures and data modelling I felt that there needed to be something to bring all these viewpoints of data delivery together, one from data automation and one from data modelling perspective. They are not the same but if a data vault is to be properly delivered it must be cognisant of all of it.
Data Vault continues to evolve and communities of data vault practitioners exist around the world. Forums and meetups focus on how data vault has been applied to specific situations and tools (which is great!) but for some attendees they often do not have access to the tools or may never encounter the unique situation. Data Vault models are being delivered with a partial view of the standards; although Data Vault implementations have evolved the standards remain consistent. Partial view in the sense that later down the line you learn that something you implemented doesn't scale and it turns out you didn't understand the full spectrum of the standard at the time of design and implementation!
Often once a Data Vault is committed to; some considerations are not thought of until you get there, for example what the categories of business rules are and how to build a Business Vault and how to get the data out of data vault! And falsely comparing dimensional modelling against data vault implementation! All of these will factor into how you design and build your data vault.
A Data Vault Alliance (DVA) exists that brings together Data Vault professionals from around the globe with decades worth of experience from delivering the classic batch-oriented data warehouse to real-time delivery. Discussions are both business and technical oriented and often the topics cover even the not-yet popular technical platforms as Data Vault is being used to deliver the modelling advantages you would expect!
Data Vault 2.0 is not complicated; and that is why it works. By keeping every component decoupled, and integrate like microservices and cloud architecture, Data Vault 2.0 can infinitely scale. There's even a checklist (chapter 12) in the book on what a data vault 2.0 automation tool must deliver in order be DV2.0 compliant!
Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts. In the assignments in this course, you can use either Oracle or PostgreSQL.
You should have some prior experience with software engineering and business intelligence. This Specialization is designed primarily for software engineering professionals seeking to enter the fields of data engineering, architecture, or big data analytics, but other experienced technical professionals are also welcome.
14) Your organization is building a collaboration platform for which they chose AWS EC2 for web and application servers and MySQL RDS instance as the database. Due to the nature of the traffic to the application, they would like to increase the number of connections to RDS instances. How can this be achieved
24) You have launched an RDS instance with MySQL database with default configuration for your file sharing application to store all the transactional information. Due to security compliance, your organization wants to encrypt all the databases and storage on the cloud. They approached you to perform this activity on your MySQL RDS database. How can you achieve this
28) A gaming company stores large size (terabytes to petabytes) of clickstream events data into their central S3 bucket. The company wants to analyze this clickstream data to generate business insight. Amazon Redshift, hosted securely in a private subnet of a VPC, is used for all data warehouse-related and analytical solutions. Using Amazon Redshift, the company wants to explore some solutions to securely run complex analytical queries on the clickstream data stored in S3 without transforming/copying or loading the data in the Redshift. As a Solutions Architect, which of the following AWS services would you recommend for this requirement, knowing that security and cost are two major priorities for the company
Option A is incorrect because Amazon Athena can directly query data in S3. Hence this will bypass the use of Redshift, which is not the requirement for the customer. They insisted on Amazon Redshift for the query purpose for usage.Option B is incorrect. Even though it is possible, NAT Gateway will connect Redshift to the internet and make the solution less secure. Plus, this is also not a cost-effective solution. Remember that security and cost both are important for the company.Option C is CORRECT because VPC Endpoint is a secure and cost-effective way to connect a VPC with Amazon S3 privately, and the traffic does not pass through the internet. Using Amazon Redshift Spectrum, one can run queries against the data stored in the S3 bucket without needing the data to be copied to Amazon Redshift. This meets both the requirements of building a secure yet cost-effective solution.Option D is incorrect because Site-to-Site VPN is used to connect an on-premises data center to AWS Cloud securely over the internet and is suitable for use cases like Migration, Hybrid Cloud, etc.
A. Server-side encryption with customer-provided encryption keys (SSE-C).B. Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3)C. Server-Side Encryption with KMS keys Stored in AWS Key Management Service (SSE-KMS)D. Protect the data using Client-Side Encryption
Option A is incorrect because No Retrieval Limit, the default data retrieval policy, is used when you do not want to set any retrieval quota. All valid data retrieval requests are accepted. This retrieval policy incurs a high cost to your AWS account for each region.Option B is CORRECT because using a Free Tier Only policy, you can keep your retrievals within your daily AWS Free Tier allowance and not incur any data retrieval costs. And in this policy, S3 Glacier synchronously rejects retrieval requests that exceed your AWS Free Tier allowance.Option C is incorrect because you use Max Retrieval Rate policy when you want to retrieve more data than what is in your AWS Free Tier allowance. Max Retrieval Rate policy sets a bytes-per-hour retrieval-rate quota. The Max Retrieval Rate policy ensures that the peak retrieval rate from all retrieval jobs across your account in an AWS Region does not exceed the bytes-per-hour quota that you set. Max Retrieval rate policy is not in the free tier.Option D is incorrect because Standard Retrieval is a process of data retrieval from S3 Glacier that takes around 12 hours to retrieve data. This retrieval type is chargeable and incurs costs on the AWS account per region wise.
Option C is incorrect because Amazon API Gateway supports RESTful APIs (HTTP and REST API) and WebSocket APIs. It is not meant for the development of GraphQL API.Option D is CORRECT because with AWS AppSync one can create serverless GraphQL APIs that simplify application development by providing a single endpoint to securely query or update data from multiple data sources and leverage GraphQL to implement engaging real-time application experiences.
33) A weather forecasting company comes up with the requirement of building a high-performance, highly parallel POSIX-compliant file system that stores data across multiple network file systems to serve thousands of simultaneous clients, driving millions of IOPS (Input/Output Operations per Second) with sub-millisecond latency. The company needs a cost-optimized file system storage for short-term, processing-heavy workloads that can provide burst throughput to meet this requirement.What type of file systems storage will suit the company in the best way
Option B is incorrect because FSx for Lustre with Deployment Type as Persistent file systems are designed for longer-term storage and workloads. The file servers are highly available, and data is automatically replicated within the same Availability Zone in which the file system is located. The data volumes attached to the file servers are replicated independently from the file servers to which they are attached. 153554b96e
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