No, you cannot use the SELECT INTO syntax for Snowflake as it does not have that statement. However, there are other alternatives that can be used instead such as CREATE TABLE as SELECT statement in order to create a table by copying or duplicating the existing table or based on the result of the SELECT query.

With CREATE TABLE as SELECT you are able to:
-Create a table with selected columns, by changing column names and data types from an existing table.
-Create a table with the result of a query or duplicated a table from an existing table.

Yes, you can perform top syntax in the snowflake database.

It can be done by using TOP and this will constrain the maximum number of rows returned by statement or subquery.
Important to Note: When you use TOP make sure that is a non-negative integer constant and also note that LIMIT is equivalent to TOP .

Furthermore, when using TOP the ORDER BY clause does not have to be used. But if you DO NOT use the clause then the results will be non-deterministic as the results within the result set are not in a specific order. So if you want to ensure that the results returned are controlled, be sure to use the ORDER BY clause.

Zero copy cloning in Snowflake is the feature that allows the duplication of an object without having to create a physical copy and without having to add any more storage costs. This can be done through the command word CLONE and this allows the creation of copies of tables, schemas, and databases. However, this feature is not limited to these things, it can also create clones of other objects such as file formats and sequences to name a few. The great thing about copy cloning is that this feature allows a lot of flexibility without having to worry about the cost.

Yes. This is really easy to do. Just be careful that if you start setting high retention periods on frequently changing tables that storage amounts and costs can increase significantly.

While they are both features that make sure you have access to your historical data, Fail-safe is a way to recover historical data in case there was a system failure or other type of failure, and Time Travel allows you to query, clone, and restore the historical data that is within the tables, schemas, or databases (allows this for 90 days).

First, what is data profiling? It can be seen as looking at and analyzing data sources in order to create summaries. This helps gather information that companies can use to help their business.
Data profiling is also important to ensure that your data is truly accurate of how your business is doing by seeing if there are any errors within the data shown.
So now, what can you use for data profiling within Snowflake? Snowflake actually has a collaboration with Talend. This collaboration ensures that when legacy data is migrating to Snowflake’s cloud data warehouse, that it is correct and completed.
However, Snowflake’s structure is made so that it can work with many other profiling tools. Some of the specific tools have been Talend Open Studio, Pentaho PDI, and Etlworks just to name a few but these all work differently.

YES. This is easy to do from the interface or from code. You just use the LIKE syntax like this: \n(okay. maybe its technically not code level DDL but it accomplishes the same thing)\nCREATE TABLE SNOWFLAKE_SOLUTIONS_TABLE_WITH_NO_DATA LIKE SNOWFLAKE_SOLUTIONS_TABLE_BEING_CREATED_LIKE_ANOTHER;

Actions such as querying, cloning, and restoring historical data from the tables, schemas, and databases are available for up to 90 days in Time Travel. However, this is within the Extended Time Travel which only comes from the Snowflake Enterprise Edition. This means that for the standard accounts the standard time for Time Travel is one day.

Yes, you can connect PySpark to Snowflake as Snowflake is compatible with both Python and Spark.
The Snowflake Connector for Spark, also known as Spark Connector, uses Snowflake within the Apache Spark system which allows Spark to read and write data to Snowflake.

Snowflake Time Travel gives access to historical data (which is any data that has either been changed, lost, or deleted).

With Time Time Travel you can also:
1. Make clones of schemas, tables, or even databases
2. Restore these schemas, tables, and databases

However, once the period of time that is defined has passed, the data will be moved into Snowflake Fail-Safe and these features can no longer be carried out.

Fail-safe protects historical data in case there is a system failure or any other failure.

Fail-safe allows 7 days in which your historical data can be recovered by Snowflake and it begins after the Time Travel retention period ends.

It is important to note that Fail-safe should only be used when all other recovery options have been tried and it can be used only by Snowflake in order to recover data that may have been lost due to any types of system failures.

Lastly, recovering data through Fail-safe can take hours to days.

In short, Time Travel does have some storage fees imposed for maintaining historical data.

The way that these fees are calculated is by each day (24 hours) from each time the data has changed. By maintaining only the necessary information to restore the roots from a table that has been updated or deleted, Snowflake lessens the amount of storage that is needed for historical data. Essentially, the only storage usage that is calculated is the percentage of the table that changed.

In order to manage the storage fees with Time Travel, Snowflake has two table types (Temporary and Transient) that don’t have the same storage fees as Permanent Tables.These two table types' maximum additional fees are limited to one day.

As a result, the maximum additional fees incurred for Time Travel by these types of tables is limited to 1 day.

If you want to connect to Snowflake from R (RStudio) you will need to use the ODBC (Open Database Connectivity) driver on either Windows, MacOS, or Linux.

Here are some steps on how to do this:

1. The first step is to create an ODBC DSN (Data Source Name)
2. Then you will test if the ODBC DSN is working
3. Lastly, the code that can be used on RStudio is:

install.packages(c("DBI", "dplyr","dbplyr","odbc"))
library(DBI)
library(dplyr)
library(dbplyr)
library(odbc)
myconn <- DBI::dbConnect(odbc::odbc(), "SNOWFLAKE_DSN_NAME", uid="USERNAME", pwd='Snowflak123')
mydata <- DBI::dbGetQuery(myconn,"SELECT * FROM EMP")
head(mydata)

First you want to make sure that the Pentaho version is the 6.1 version or above.

Next, these steps will help you create the JDBC connection to Snowflake through Pentaho:

1. You will need to download the Snowflake JDBC driver to the workstation while at the same time running Pentaho
2. Then you will copy Snowflake_jdcb.jar to the Pentaho folder:

-On Windows this will be: C:\ProgramFiles\pentaho\design-tools\data-integration\lib
-On Mac OS this will be: …/pentaho/design-tools/data-integration/lib

3. Lastly, you will configure a Generic Database connection within Pentaho through:
Url: jdbc:snowflake://.snowflakecomputing.com:443/
Driver Class Name: net.snowflake.client.jdbc.SnowflakeDriver

Airflow is a platform designed to automate, schedule, and monitor workflows in an automated way. It is mainly used for data processing pipelines, computational workflows, and ETL processes.
The tool can be customized to meet each need and this makes other tasks easier to complete.

Some of the other features of Airflow include:
1. Directed Acyclic Graphs and they represent a workflow in Airflow
2. Easy access and interaction with logs
3. Set up alerts
4. Monitoring interface
5. PythonOperator
Furthermore, some of the things that will be needed for Airflow are Python, a Snowflake account and access to the latest Apache-Airflow.

To integrate Apache-Airflow with Snowflake you will:
1. Configure Apache-Airflow with snowflake connection.
2. Open “localhost:8080” within the browser
3. Go under “Admin” and then “Connections”
4. Click on the + symbol and then add a new record
5. Lastly, choose the connection type as “Snowflake”

Then you will need to create a DAG file:
1. Go to the folder you have chosen as your AIRFLOW_HOME
2. Find the DAGs folder within subfolder_dags
3. Inside the DAG folder you will need to paste the Python file
4. Create a Python file with the name “snowflake_airflow.py”

Workflows will automatically be picked up and scheduled to run.

You would use the command CREATE STREAM (or REPLACE STREAM) and it would create a new stream in the current schema or it would replace an existing stream. An example would be: create stream mystream on table mytable;

The command also supports:
-CREATE STREAM...CLONE (to create a clone of the existing stream)

However, if change tracking hasn't been enabled then only the table owner can make the first stream on the table. Adding more streams would require a role that has been granted these privileges:
-Database: USAGE
-Schema: CREATE STREAM, USAGE
-Source table: SELECT

Snowflake extends the data retention period for the source table temporarily when a stream is not consumed regularly.

Regardless of which edition of Snowflake you have on your account, if the data retention period for the table is less than 14 days, behind the scenes it is extended to the smaller of the stream transactional offset or 14 days (if the data retention period for the table is less than 14 days). But your monthly storage charges will be impacted if you extend the data retention period.

A stream's main cost is the processing time used by a virtual warehouse to query the stream. You see these charges on your bill as Snowflake credits.

Within Snowflake, it would be a resource monitor. Using resource monitors, you can set limits on how many credits are consumed through user-managed virtual warehouses and virtual warehouses used by cloud services.

How many credits are consumed, would be based on the warehouse size and how long it runs.

The resource monitor would set limits for a particulate interval or date range. When these limits that are set are reached and/or approaching, the resource monitor would send alert notifications and/or suspend the warehouses.

Account admin. are the only ones who can create resource monitors, but account admin. can allow users with other roles to view and modify resource monitors through SQL.

Collaborating on a single copy of data has never been easier. Snowgrid is the perfect platform that allows you to discover and share data between teams, allowing for instant access to up-to-date information. The platform has built in governance controls and policies that follow the data, allowing for secure access and regulation across employees. The cross-cloud connectivity allows businesses to operate quickly and efficiently, creating a better customer experience as a result. Unlock more value from your data today!

The Data Cloud or the Snowflake Data Cloud is an overall Data Platform for analytical data workloads. Currently Snowflake is positioning their overall Data Cloud to handle 6 workloads:

• Data Warehousing
• Data Engineering
• Data Lake
• Data Science
• Data Applications
• Data Exchange

I wrote a more in depth article on the what I think the Snowflake Data Cloud is:
Snowflake now also has the overall Snowgrid which is the overall connected Data Mesh of all of Snowflake's accounts across all cloud providers and regions. Snowflake has achieved this through replication features across clouds.