by Vyshnavi. CR
Looker Tips and Tricks - Hub and Spoke Implementation
In a large organization when we have a huge number of users, the requirements are difficult to handle. Looker helps us provide a generic data model and allow the users for appending or eliminating the code. The Hub and Spoke implementation help us achieve this.
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Hub and Spoke Implementation
by Ramana Kumar Gunti
Data Security in Snowflake
Data security is probably the number one topic on everyone's head when it comes to moving your data into the cloud. This is especially true if you are new to the cloud and don't have a seasoned technical team that understands cloud security. In this article, we will talk about how does Snowflake help? So let's get started.
While the world is moving towards Cloud Computing, Cloud Storage, and other Cloud Technologies which run on Public networks, it is important to secure and safeguard the data.
To achieve data security, Snowflake provides a handful of methodologies.
- Data Encryption
- RLS – Row Level Security
- CLS – Column Level Security- Data Masking
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by Ramana Kumar Gunti
Data Security in Snowflake using CLS
Large companies and professional businesses have to make sure that data is kept secure based on the roles and responsibilities of the users who are trying to access the data. One way to protect data is to enforce “Column Level Security” (CLS) to ensure that people can only access what they are supposed to see.
In this article, We will show how the Column Level Security can be implemented by using one of the Snowflake feature Data Masking Policy. So, Let’s get started!!
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by Ramana Kumar Gunti
Data Security in Snowflake using RLS
Large companies and professional businesses have to make sure that data is kept secure based on the roles and responsibilities of the users who are trying to access the data. One way to protect data is to enforce “Row Level Security” (RLS) to ensure that people can only access what they are supposed to see.
Snowflake working on fine-tuning the Row Access policies for general availability. In this article, we will show an approach to restrict the Row Level Data to Authorized users by using the Snowflake Functions CURRENT_ROLE , CURRENT_USER
So, Let’s get started!!
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by Ramana Kumar Gunti
Data Sharing in Snowflake
In this article, we will talk about Snowflake data sharing which enables account-to-account sharing of data through Snowflake database tables, secure views, and secure UDFs. So let's get started.
Snowflake data sharing is a powerful yet simple feature to share the data from one account and to use the shared data from another account. The data producer can provide access to his live data within minutes without copying or moving the data to any number of data consumers. The data consumer can query the shared data from data producer without any performance bottle necks thanks to snowflakes multi-cluster shared data architecture.
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by Abdul Mathin Shaik
Oracle Data Integrator with Snowflake
In this article, we will talk about how to load the data into the Snowflake data warehouse using Oracle Data Integrator ODI. So let's get started.
Lately, one of our Snowflake customers was looking to use Oracle Data Integrator to load data into Snowflake from oracle DW. I have been working with Oracle applications such as ODI for years now, so I wanted to explore how to configure ODI to work with Snowflake.
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by Manju Das
Manual performance optimization in Denodo – Most relevant and most used techniques
We are going to discuss how to optimize the Denodo query to make the most of the optimization in the Denodo platform. Below are the most powerful query optimization techniques that we can apply in Denodo platform manually which will increase the performance significantly.
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by Pushkar Reddipalli
BigQuery Best Practices to Optimize Cost and Performance
BigQuery is Google's fully managed, petabyte scale, low cost analytics data warehouse. It allows you to execute terabyte-scale queries, get results in seconds, and gives you the benefits of being a fully managed solution. BigQuery and other Google Cloud Platform (GCP) services offer "pay per use" pricing. If you know how to write SQL Queries, you already know how to query it. In fact, there are plenty of interesting public data sets shared in BigQuery, ready to be queried by you.
In this blog post, I will guide you through best practices to implement Google BigQuery to control costs and optimize performance regardless of the level of development experience you have. To start working with BigQuery, you must first upload your data, which means that you will also use it as storage, then verify this data in some way to create your models or create reports. In the next section, we will analyze best practices for these main actions of loading, querying and storing.
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Solving Data Lake Challenges with Databricks Delta Lake
What is Data Lake:
Data lake drive is what is available instead of what is required. The typical data lake is a storage repository that can store a large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytical performance and native integration to implement Machine Learning and AI models to make profitable predictions. This enables the 360 degrees view of customers and makes the analysis more robust to offer a competitive advantage to the organization.
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Data Lake Challenges with Databricks Delta Lake
by Balachandra Sridhara
Snowflake Secure Views vs. Views in Oracle
What is a view in a Database?
A view is the result set of a SQL query built on top of one or more tables. Views do not store the data; however, during run time view definition (SQL) will be executed, and the result will be shown to the user. Please find the Wikipedia link for more information View (SQL).
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