MiNdLiNkS is only the Training Source for enabling Training & Practice on Snowflake Cloud Data Warehouse Tool in the World’s Commercial Training Market.
What is the concept of Cloud and Why everyone should be transitioning to the Cloud? – Whatever the major 4 challenges all clients have been facing irrespective of domain or technology, i.e., 1. Resource Optimization 2. Cost Optimization 3. Process Optimization 4. IT Infrastructure Optimization, Cloud entered into the market as a One-Stop & Common Solution. Right from the licensing part, the Cloud mitigates cost in implementing technology. 90% of the resources can be optimized which is a big advantage for any client, as maximum features come within the technology whereby just dragging & dropping, implementations take place in a short period. As business is consistently and persistently changing process, releases expected to deliver very faster, we can optimize a lot of processes in any cloud technology having functionality pre-defined where technology comes as a product. It became a big bang for all clients with On-premise to facing issues of recurrent investments on changing versions frequently which completely pruned down by the Cloud. Not only the Softwares, Databases, and Platforms but also Infrastructure also comes as a service where it’s just the Pay-AS-You-GO concept. This is only the reason where right from small technology providers to Microsoft, Oracle, and SAP range giant technology providers have been replacing their legacy On-premise tools & technologies by the new cloud solutions. So, it is mandatory for everybody to move to the Cloud to retain their job.

Why only MiNdLiNkS? – MiNdLiNkS is only the Training Company, has been aggressively marketing Cloud technologies training as this is only the Training Organization operating by above Delivery Managers or Competency Lead level directors. MiNdLiNkS was only the introducer for all Cloud Trainings in the world’s commercial market. We always encourage people to for the new to create their own demand on their profile instead of being a sheep in a drove. It is easy to crack the interviews and to work from the Day-1 as an industry-ready professional, going with the new technologies as technical understanding would be very less across the companies. This is where instead of you-search for companies, companies would hunt you. Our Motto is guiding the people first and training them next. We directors at this level of expertise counsel the people assessing candidates’ profiles properly prior to suggesting them their career-launching vehicle. We redefined training strategies and created a domain for the training when the rest of the so-called training institutes are going with all old pickles. Our Goal is, Candidate should get the job and he has to work from day-1 without any tensions and thus, we transitioned from conventional training process to the Real-Time Project based training so that students can be knowing what exactly they have to do once they get into the project. Not Just Training, giving support to finish Certifications to finish with maximum score in a secured environment. The best what you can get from MiNdLiNkS is giving Job Assistance, right from preparing the CV with al current running projects with proper clients and giving MNC references. MiNdLiNkS got tie-ups with more than 140 US staffing consultancies and supporting them in all Training, Certifications, and Proxy Support.

What is the Snowflake Designer? – Snowflake ‘s unique architecture empowers data analysts, data engineers, data scientists and data application developers to work on any data without the performance, concurrency, or scale limitations of other solutions. Snowflake is a single, near-zero maintenance platform delivered as-a-service. It features compute, storage, and cloud services layers that are logically integrated but scale independent from one another, making it an ideal platform for many workloads.

Secure and governed by design, and compatible with popular ETL, BI, and data science tools, Snowflake enables data professionals to support many datawarehouses, data lake, data engineering, and data science workloads with virtually unlimited concurrency. Snowflake is also a powerful query processing back-end platform for developers creating modern data-driven applications.

Metadata is also automatic. What’s more, metadata processing with Snowflake is automatic and does not compete with the computing resources running your queries. This means Snowflake can scale near-infinitely as your computer resources scale-out.

What is the Architecture of Snowflake? – Snowflake is built on a patented, multi-cluster, shared data architecture created for the cloud to revolutionize data warehousing, data lakes, data analytics, and host of other use cases. Snowflake is a single platform comprised of storage, compute, and service layers that are logically integrated but scale infinitely and independently from one another.

What about Storage? – Built on scalable cloud blob storage, the storage layer holds all the diverse data, tables, and query results for Snowflake. Maximum scalability, elasticity, and performance capacity for data warehousing and analytics are assured since the storage layer is engineered to scale completely independent of computing resources. As a result, Snowflake delivers unique capabilities such as the ability to process data loading or unloading, without impacting running queries and other workloads.

Under the covers of the storage layer, Snowflake utilizes micro-partitions to securely and efficiently store customer data. When loaded into Snowflake, data is automatically split into modest-sized micro-partitions, and metadata is extracted to enable efficient query processing. The micro-partitions are then columnar compressed and fully encrypted, using a secure key hierarchy.

What is Compute Layer in Snowflake? –  The compute layer is designed to process enormous quantities of data with maximum speed and efficiency. All data processing horsepower within Snowflake is performed by virtual warehouses, which are one or more clusters of computing resources. When performing a query, virtual warehouses retrieve the minimum data required from the storage layer to satisfy queries. As data is retrieved, it’s cached locally with computing resources, along with the caching of query results, to improve the performance of future queries.

In addition, and unique to Snowflake, multiple virtual warehouses can simultaneously operate on the same data while fully enforcing global system-wide transactional integrity with full ACID compliance. Read operations (SELECT) always see a consistent view of data, and write operations never block readers. Transactional integrity across virtual warehouses is achieved by maintaining all transaction states within the services layer metadata.

What is Services Layer in Snowflake? –  If the compute layer is the brawn of Snowflake, the services layer is the brain. The services layer for Snowflake authenticates user sessions, provides management, enforces security functions, performs query compilation and optimization, and coordinates all transactions. The services layer is constructed of stateless computing resources, running across multiple availability zones, and utilizing a highly available, distributed metadata store for global state management.

The services layer also provides all security and encryption key management and enables all Snowflake SQL DML and DDL functions. Queries are compiled within the services layer and metadata is used to determine the micro-partition columns that need to be scanned. Metadata processing is powered by a separate sub-system that’s also integrated with all of Snowflake. As a result, all operational states maintained within the services layer, which performs transaction coordination across all virtual warehouses (our compute clusters) can process without robbing you of query compute resources.

What are the benefits of Snowflake Architecture?
1. Multi-cluster, Shared Data: Snowflake’s multi-cluster, shared data architecture is designed to process enormous quantities of data with maximum speed and efficiency. All data processing horsepower within Snowflake is performed by one or more clusters of computing resources. When performing a query, these clusters retrieve the minimum data required from the storage layer to satisfy queries. As data is retrieved, it’s cached locally with computing resources, along with the caching of query results, to improve the performance of future queries. In addition, and unique to Snowflake, multiple compute clusters can simultaneously operate on the same data while fully enforcing global, system-wide transactional integrity with full ACID compliance. Operations always see a consistent view of the data and write operations never block readers. Transactional integrity across compute clusters is achieved by maintaining all transaction states within the metadata services layer.
2. Micro partitioning: Snowflake utilizes micro-partitions to securely and efficiently store customer data. When loaded into Snowflake, data is automatically split into modest-sized micro-partitions, and metadata is extracted to enable efficient query processing. The micro-partitions are then columnar compressed and fully encrypted using a secure-key hierarchy. Built on scalable cloud blob storage, the storage layer holds all the diverse data, tables, and query results for Snowflake. Maximum scalability, elasticity, and performance capacity for data and analytics are assured since the storage layer is engineered to scale completely independent of computing resources. As a result, Snowflake delivers unique capabilities such as the ability to process data loading or unloading, without impacting running queries and other workloads.
3. As a Service: Snowflake eliminates the administration and management demands of traditional data platforms. Snowflake is a true data platform-as-a-service running in the cloud. With built-in performance, there’s no infrastructure to manage or knobs to turn. Snowflake automatically handles infrastructure, optimization, availability, data protection, and more, so you can focus on using your data, not managing it. Per-second, usage-based pricing for computing and storage means you only pay for the amount of data you store and the amount of computing processing you use. Say goodbye to upfront costs, over-provisioned systems, or idle clusters consuming money.
4. Data Warehouse Built for Any Cloud: Separation of services from storage and compute allows multiple, compute clusters to simultaneously operate on the same data. Concurrency is virtually unlimited and can instantly scale with a multi-cluster warehouse. Full ACID transactional integrity is maintained across separate compute clusters. Queries always see a consistent view of data, while transaction commits are immediately visible to new workloads running on the platform. Activity in one computer cluster has zero impact on all other compute clusters. For example, data science in a compute cluster does not impact performance on queries running in other compute clusters, even when they are accessing the same data. Time travel enables any select statement or zero-copy clone to view the database in a retained, consistent, “as of” state up to 90 days in the past. The default is 24 hours retention. Zero-copy clones of terabyte databases or tables happen in a matter of seconds and without incurring the extra storage cost
A clone is a fully logical replica of the original object but with an independent lifecycle.
5. Performance and Throughput: Snowflake outperforms traditional methods for executing data workloads. Compute resources scale linearly in Snowflake, while efficient query optimization delivers answers in a fraction of the time of legacy cloud or on-premises systems. Performance challenges can be addressed in seconds. You can specify the size of a compute cluster based on the performance you initially require. But you can resize at any time and even while a workload is running. Multi-cluster warehouses deliver a consistent SLA to an unlimited number of concurrent users. Automatic clustering eliminates manual re-clustering of data when loading new data into a table. With materialized views, users experience improved query performance of workloads composed of common, repeated query patterns. As concurrent workloads increase, Snowflake automatically adds to compute clusters and distributes queries across them, removing the hassle of manually re-clustering data. Clusters pause when the workload decreases. Charges only accrue for the active cluster, so you only pay for what you use and by the second. Plus, you can pause compute clusters at any time.
6. Storage and Support for All Data:
Storage is inexpensive and can scale almost infinitely. Snowflake is the optimal platform for warehousing data, delivering cost-effective and highly performant support for multi-petabyte databases. All storage costs are based on actual usage for compressed data and measured in TB stored per month.
You can query both structured and machine-generated, semi-structured data (i.e., JSON, Avro, XML, Parquet) using relational SQL operators with similar performance characteristics as if querying structured data. Loading semi-structured data is painless. Schemas are dynamic and are automatically discovered during load. This support for dynamic schemas enables efficient query execution using natural extensions to SQL.
With Snowflake, there’s no need to implement separate systems to process structured and semi-structured data. You can eliminate complex Hadoop and data warehouse pipelines. Snowflake can perform both roles much more efficiently and with better business results at a lower cost.
7. Availability and Security: Achieve high availability with Snowflake ‘s scale-out architecture, which is fully distributed across multiple Amazon, Azure, and Google availability zones. Snowflake can continue operations and withstand the loss of availability due to hardware failure. The system is designed to tolerate failures with minimal impact to our customers. Snowflake is secure by design. All data is encrypted in motion, over the internet or direct links, and at rest on disks. Snowflake supports two-factor and federation authentication with single sign-on. Authorization is role-based. You can enable policies to limit access to predefined client addresses. Snowflake is SOC 2 Type 2 certified on both AWS and Azure and support PHI data for HIPAA customers is available with a Business Associate Agreement. Additional levels of security, such as encryption across all network communications and virtual private or dedicated isolation, are also available.
8. Sharing and Collaboration Across all Data: Snowflake’s Secure Data Sharing enables you to share the data within your account with other Snowflake users but without having to copy or transfer data from the data provider’s account to the data consumer’s account. Instead, you grant secure and curated access to read-only copies of your data. Accounts that receive shared data only pay for the computing resources they use to consume the data. Data shared from a Snowflake data provider can easily be combined with data from the Snowflake data consumer’s account without laborious effort or third-party tools. Avoid the burdens and complexities of decades-old email, FTP, and EDI technologies with Snowflake. Simply decide what you want to share with your data consumers, and share the data through easy-to-use SQL functions.
9. Connections: You will get a 30-day free trial with all features currently, you can use your official email id it would be good, but you can also use your personal email id also. But it is announced that they are about to stop giving sandbox. So learning it as soon as possible would be rather good.
10. Use Cases:
Data Warehouse Modernization: Take advantage of a modern architecture that brings together all your data in one place and makes it available to all your users and applications.
Insights from your Data Lake: Easily and affordably combine diverse data for exploration, experimentation, and refinement without sacrificing performance.
Accelerating Analytics: Deliver rapid data insights at any scale of users, workload, and concurrency, and without the need to manually tune and optimize your data platform.
Data Exchange: Seamlessly share data both inside and outside of your organization without copying, transferring, or manually transforming data.
Enabling Developers: From data science to embedded applications, see how Snowflake enables developers to build the applications and services that put data to full use.
Data Engineering: Load data continuously with streamlined data transformation thanks to robust and integrated data pipelines using Snowpipe.
Data Science: Simplify and accelerate machine learning and artificial intelligence with a centralized source of high-performance data and compute power that scales instantly and near-infinitely.

What does a course entail? – The length and content of the Snowflake course will be vast and vary and well planned by our Senior Technical Architect cum Trainer. Each concept assumes few proposed hours of study per day and it is strongly advised to schedule no other training during the course time as this impedes the ability to learn the content and to get expertise from the course.

Do I need training? Can’t I just learn on the Job? – Although this practice does occur with any New & Emerging Technology or Tool nowadays, 99% of People in the market don’t know Snowflake’s name, and companies are literally hunting for Snowflake resources and nowhere we have training provisioning except MiNdLiNkS. And as it’s a Cloud, everyone won’t be having Cloud Access to Teach and Practice. So, who can teach and where can you learn? without taking the training from a really Real-Time expert working for partner companies having superuser power, it is not possible to get to know the internals of Snowflake. In addition, there are many nuances to each solution that would not be known without taking official training.

What sort of experience do I need? – There is no particular experience required; Snowflake itself is new to the market and companies haven’t any other alternative to go for when they are short-staffed with required resources. Just learn, practice, get support from MiNdLiNkS, and see jobs available for U.

Are there any prerequisites to take the training? – As mentioned above, to undertake any course training you won’t be having any prerequisites to learn. MiNdLiNkS asks you to meet us just with 2 B’s, a Blank mind, and a Blind practice. Just give our Corporate Ladders to fill those blanks. Having a basic understanding of Data warehousing will be good but we will cover that also in the inception stage of training.

Do we have Certification and what sort of support will be from MiNdLiNkS? – Yes, we have different certifications from Snowflake. And Client billing is very huge for certified people. Certified people are stars in the sky of the Job Market. MiNdLiNkS can offer you 100% support to finish your certification with maximum score in a secured environment.

Who should attend? – Data Warehousing, Informatica, Informatica On-Demand, Cognos, Abintio, DataStage, ETL, Mainframes, MySQL DB, SQL Server DB, SQL Server DBA, Oracle DB, Oracle DBA, Hadoop, Big Data, Data Scientists, Data Analysts, KarmaSphere, Azure Admins, AWS Admins, Talend, Endeca, Matillion, IFTTT, Zapier, Blendo, Stitch, Panoply, BigQuery, Redshift, Oracle 18c, PostgreSQL, MongoDB, Teradata, Solver, Amazon RedShift, Apache Hive, Datamart, Greenplum, SAP BW, SAP BW/4HANA, IBM Infosphere Warehouse, Netezza, Tivoli, Talend, Oracle DAC, Any ETL consultants or learned people, Any Data BASE Developers or learned people, Any DataBase Admins or learned people, Any BI and Reporting Tools Professionals or learned people, Any Data visualization consultants or learned people, People who are jobless and want to make their career with NO CODING.

What is our Training plan? – 1 Month of daily sessions includes Training on concepts, Assignments, Live Case Studies, Real-time Working Scenarios, Expertise on Snowflake Software, End-to-end Resume building & Presentation, Genuine FAQs & Generalized worked-out answers, Informative material documents, Tech Support, Mock Interviews and Consultation (INTERNAL REFERENCES in renowned CMM level companies) with 100% Placement Assistance • 100% pass guaranteed Certification support from MiNdLiNkS.

What about Courseware? – We determined the requirements of IT industries and designed the courseware by our panel of Mentoring Board while considering Snowflake standards and Industrial ongoing changes and valuable feedbacks from senior competency heads.

Topics:

Getting Up to Speed on Cloud Data Warehousing

  • Data warehousing: past to present
  • Understanding the benefits of a cloud data warehouse
  • Recognizing where cloud data warehousing fits in today’s economy

Why the Modern Data Warehouse Emerged?

  • Adapting to increasing demands for data access and analytics
  • Adjusting to how data is created and used today
  • Tackling the challenges with new and improved technologies

The Criteria for Selecting A Modern Data Warehouse

  • Choosing the right data warehouse solution
  • Getting a high performance-to-price ratio
  • Making data security and protection a priority

Comparing Cloud Data Warehouse Solutions

  • Evaluating differences between cloud data warehouse options
  • Considering factors that affect performance
  • Choosing a solution that ensures data protection and security
  • Gauging your savings in administrative costs

Six Steps to Getting Started with Cloud Data Warehousing

  • Listing your data warehouse needs and success criteria
  • Considering all factors in the total cost of ownership
  • Taking your data warehouse for a test drive before you buy

Introduction to Snowflake

  • Key Concepts & Architecture
  • Cloud Platforms
  • Snowflake Editions
  • Overview of Key Features
  • Overview of the Data Lifecycle
  • Continuous Data Protection

Connecting to Snowflake

  • Overview of the Ecosystem
  • SnowSQL (CLI Client)
  • Installing SnowSQL
  • Configuring SnowSQL
  • Connecting Through SnowSQL
  • Using SnowSQL
  • JDBC Driver
  • Downloading / Integrating the JDBC Driver
  • Configuring and Using the JDBC Driver
  • JDBC Driver Diagnostic Service
  • ODBC Driver
  • Downloading the ODBC Driver
  • Installing and Configuring the ODBC Driver for Windows
  • ODBC Configuration and Connection Parameters
  • ODBC Driver API Support
  • ODBC Driver Diagnostic Service
  • Client Considerations
  • Diagnosing Common Connectivity Issues

Loading / Unloading Data Into/From Snowflake

  • Overview of Data Loading/Unloading
  • Data Loading/Unloading Considerations
  • Preparing to Load/Unload Data
  • Bulk Loading/Unloading from a Local File System
  • Bulk Loading/Unloading from Amazon S3
  • Bulk Loading /Unloading from Microsoft Azure
  • Loading Continuously Using Snowpipe
  • Loading Using the Web Interface (Limited)
  • Querying Data in Staged Files
  • Querying Metadata for Staged Files
  • Transforming Data During a Load

Using Snowflake

  • Using the Web Interface
  • Virtual Warehouses
  • Databases, Tables & Views
  • Queries
  • Date & Time Data
  • Semi-structured Data
  • Binary Data
  • Snowflake Time Travel & Fail-safe

Sharing Data in Snowflake

  • Introduction to Data Sharing
  • Data Providers
  • Getting Started with Data Sharing
  • Working with Shares
  • Using Secure Objects to Control Data Access
  • Managing Reader Accounts
  • Configuring a Reader Account
  • Data Consumers

Managing Your Snowflake Account

  • System Usage & Billing
  • Understanding Snowflake Credit and Storage Usage
  • Understanding Snowflake Data Transfer Billing
  • Monitoring Account-level Credit and Storage Usage
  • Working with Resource Monitors
  • Parameter Management
  • User Management

Customer Specific Topics

  • Informatica Cloud Services and Snowflake Integration
  • Introduction to Snowflake Connector
  • Snowflake Connector Task and Object Types
  • Informatica Cloud Hosted Agent
  • Snowflake Connections
  • Snowflake Connection Properties
  • Mappings and Mapping Configuration Tasks with Snowflake Connector
  • Snowflake Objects in Mappings
  • Pushdown Optimization
  • Snowflake Sources in Mappings
  • Key Range Partitioning
  • Snowflake Targets in Mappings
  • Snowflake Lookups in Mappings
  • Rules and Guidelines for Snowflake objects

Mr. Raghu Ram, Certified, working as Senior Solution Architect, having 15 plus years of extensive experience and MiNdLiNkS endorsed Training Professional

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