Data Warehousing and Database concepts- The basics
The basic concept of a Data Warehouse is to facilitate a single version of truth for a company for decision making and forecasting. A Data warehouse is an information system that contains historical and commutative data from single or multiple sources. Data Warehouse Concepts simplify the reporting and analysis process of organizations.
- Cloud Data Warehouse vs Traditional Data Warehouse Concepts
- Cloud Data Warehouse Concepts
- Conclusion: Traditional vs. Data Warehouse Concepts in Brief
- Data Warehouse Architecture: Traditional vs. Cloud
- New Data Warehouse Architectures
- Data Mart vs. Data Warehouse
- The Difference Between a Data Warehouse and a Database
- Data Lake vs. Data Warehouse
- 12 Datawarehouse cloud tools
- Data Warehouse Testing
Modern Data Warehouse
A modern data warehouse lets you bring together all your data at any scale easily, and to get insights through analytical dashboards, operational reports, or advanced analytics for all your users.
- BI and Data Warehousing
- Data Warehousing and Data Mining
- The New EDW: Meet the Big Data Stack
- Build An ETL Process with Examples
- ETL – load data into AWS Redshift
- Full vs Incremental Loading in ETL
- Best Cloud-Based and Open Source Tools
- Get Started with ETL
- ETL Testing
- Redshift Architecture, Pricing, and Performance
- Redshift Columnar Storage
- Redshift Cluster 101
- Snowflake and the Future of Data Warehousing
- Google BigQuery Architecture
Cloud Basics and Why choose the Cloud Warehouse
Due to their architecture, cloud-based data warehouse offers some major advantages over the traditional systems, such as: Scalability; Reliability; Security; Adaptability; Many organizations cite a lack of resources and expertise as barriers to implementing an on-site data warehouse solution. This is where cloud data warehouses become a preference.
- What is cloud computing?
- Cloud Computing Service Models – IaaS, PaaS, SaaS
- Cloud Computing Deployment Models – Public, Private & Hybrid
- Cloud Computing Basics – Compute
- Cloud Computing Basics – Storage
- Cloud Computing Basics – Network
- Cloud Computing Basics – Serverless
- What is a cloud datawarehouse?
Introduction To Snowflake
Introduction to the powerful and unique features that Snowflake provides for ensuring your data is protected, secure, and available.
Connecting To Snowflake
This guide will walk you through how to connect to Snowflake. You may need to contact the administrator of your Snowflake account if you lack some of the credentials discussed below.
Loading / Unloading Data Into/From Snowflake
These topics describe the concepts and tasks for Data Loading /unloading (i.e. exporting) data from Snowflake tables. Key concepts related to data unloading, as well as best practices.
- Overview of Data Loading/Unloading
- Data Loading/Unloading Considerations
- Preparing to Load/Unload Data
- Bulk Loading/Unloading from Amazon S3
- Bulk Loading/Unloading from a Local File System
- 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
Database Objects and Querying
A database object is any defined object in a database that is used to store or reference data. Anything which we make from create command is known as Database Object. Some of the examples of database objects are : view, table, sequence, indexes, etc.
Sharing Data In Snowflake
Snowflake data providers can share data that resides in different databases by using secure views. A secure view can reference objects such as schemas, tables, and other views from one or more databases, as long as these databases belong to the same account.
Managing Your Snowflake Account
These topics describe the administrative concepts and tasks associated with managing your account in Snowflake. These topics are intended primarily for administrators (i.e. users with the ACCOUNTADMIN, SYSADMIN, or SECURITYADMIN roles). Account Identifier.
Welcome to Advanced Topics. The goal of this course is to provide you with a deeper understanding of optimizing the usage of snowflake.
- Informatica Cloud Services and Snowflake Integration
- Informatica Snowflake Connector
- Informatica Snowflake JDBC Connection
- Informatica Snowflake Key Range Partitioning
- Informatica Snowflake Objects in Mappings
- Informatica Snowflake Sources in Mappings
- Informatica Snowflake Targets in Mappings
- Informatica Snowflake Lookups in Mappings
- Data Clustering
- Clustering Keys & Clustered Tables
- Using the Spark Snowflake Connector
Snowflake and the Future of Data Warehousing
The future of data warehousing has been a popular topic in IT conversations. The outright evolution in technology has seen a recession from on-premise servers and expensive hardware to cloud-based infrastructure devoid of complicated installations. In other words, the entire IT field has been given a facelift.
The move to cloud-based technology in all areas of enterprise software has been trending for years, but migration has never been more pervasive. As modern consumers increasingly take to the convenience of digital experience, companies are progressing through their legacy technology into advanced virtualization and data consumption. Data warehousing (DW), as the backbone of business intelligence (BI) and analytics, is heavily targeted for that initiative.
The future of DW lies in serverless infrastructure. On-premise servers and hardware are becoming antiquated, and as their presence diminishes, the constraints and typical difficulties of acquisition and management go with them.
Usurping the throne of DW are platforms with cloud-based data lake architecture making the real-time data availability simple and efficient. While we contemplate the reality of this new DW environment, one company that comes to mind is Snowflake.
Snowflake has proven to be one of the most compelling players in the game as an up-and-coming leader in Gartner’s Magic Quadrant for Data Management Solutions for Analytics in 2019. Their products have gained traction with companies of all industries because they’re modernizing Data Warehousing-as-a-Service (DWaaS) with real-time analytics in a unique virtual warehouse. The major attraction is its simple, yet powerful, three-layer architecture of database storage, query processing, and cloud services with the ability to scale independently.
When combined with a data lake — for example, Amazon Web Services (AWS) S3, Apache Hadoop, or Microsoft Azure— data is encrypted, compressed, distributed, and geo-redundant, making it exceptionally durable and available for extensible access. Opposed to on-premise warehousing, Snowflake’s virtual warehouse has versatile scaling options and allows for concurrent data loading and querying. Finally, their cloud services alleviate the need for additional resources by including all authentications, sessions, SQL compilation, and more as a part of their offering. (A detailed explanation of Snowflake’s architecture is available here.)
Another reason why Snowflake has become a popular option for DWaaS is that the platform comes as an on-demand run-time service, which means you only pay for the time Snowflake is running. While initial pricing is subject to the amount of data you store, the real savings come from the ability to analyze, report, and perform other business-critical activities without accruing additional fees from an idle, or “paused,” database.
Snowflake is emerging as a strong competitor against the more prominent names in Gartner’s Magic Quadrant — such as Amazon Redshift and Google BigQuery — particularly for their pricing model, which gives companies with smaller budgets an opportunity to leverage a powerful querying system without big name costs. Nonetheless, there’s a time and place for its implementation.
While its cost, architecture, and performance are substantial, there are technical prerequisites that can hinder an immediate migration. Although Snowflake supports a variety of commercial BI tools — for instance, Power BI and Tableau — your specific tool may not be on their radar yet. Additionally, you may not have all your ducks in a row when it comes to data quality and data stacks. Before undertaking DW virtualization, the data pipeline needs to be finely tuned.
Platforms like Snowflake are revolutionizing the way data is stored and consumed. Although there’s still a need for on-premise DW services, the future of DW lives within the vast potential of virtualization. From flexibility and durability to premiere performance and scalability, competing with virtual DWs is becoming harder and harder.
For right now, the technology offered by Snowflake plays as much of a role in modern DW as Teradata and other big-name competitors. The choice between the two depends on your organization’s needs and the style of innovation your team is looking to deploy.