We will discuss what is snowflake etl in this post. Discovering the special qualities of the Snowflake Data Warehouse is made easier by this post. You will also receive a brief run-through of how certain key Snowflake features, such as schema-less JSON/XML loading, time travel, cloning, data clustering, etc., are implemented. Additionally, you will consider Snowflake’s advantages over competing data warehouse providers. Let’s get going.
Table of Contents
When tables are queried, Snowflake ensures the optimal performance by handling all database administration. It is sufficient to build tables, load the data, and run queries on them. No need to do vacuum operations like Redshift or construct partitions and indexes like in RDBMS.
Snowflake, which is an ETL tools for data warehousing, stores the data in a columnar table that is separated into a number of contiguous storage units called micro-partitions, which range in size from 50 MB to 500 MB of uncompressed data. There is more information about micro-partitions here. Instead of being static and maintained by people like in traditional databases, partitions are automatically and dynamically managed by the Snowflake Data Warehouse.
The idea of sort-key, which is included in the majority of MPP Databases, is comparable to data-clustering. Column data with the same values are co-located in the same micro-partition as data is put into Snowflake. Because the entire micro-partition can be skipped if a value does not exist in the micro-partition’s range, this makes it easier for Snowflake to quickly filter out data during data scan.
However, there is a concept of the clustering key that can be defined on the table by the user and Snowflake uses this key to execute the clustering on the table if a user want to perform manual clustering. This is only practical for really large tables.
Other crucial snowflake characteristics
Snowflake is presently well-liked in the marketplace. Due to its unique qualities, it has become more well-known and distinguished from other Data Warehouses. Popular Snowflake characteristics include:
One of the distinctive Snowflake elements is time travel. You may follow the evolution of data through time by using time travel. All accounts have access to this Snowflake feature, which is free and enabled by default for everyone. Additionally, this Snowflake feature enables you to retrieve a Table’s historical data. Any moment throughout the previous 90 days, one can access the table’s appearance.
Cloning is another significant Snowflake characteristic. The clone capability allows us to quickly duplicate any Snowflake object, including databases, schemas, tables, and other Snowflake objects, in almost real-time. This is made possible by the architecture of Snowflake, which stores data as immutable in S3 and versions changes before saving them as metadata.
Therefore, cloning an object actually involves editing its metadata rather than duplicating its storage contents. In this case, using the Clone tool, you may quickly produce a clone of the whole Production database for testing purposes. Cloning is thus a key characteristic of snowflakes.
Fail-Safe is yet another crucial Snowflake feature. In the event of disasters like disk failures or other hardware problems, Fail-Safe makes sure that historical data is protected. In the event of a disaster, Snowflake offers 7 days of Fail-Safe protection for data that can only be recovered by Snowflake.
Fail-Safe After the time travel period has passed, a 7-day period begins. The total recuperation time for a table with 90 days of time travel will be 97 days. Fail-Safe, however, cannot be restored by users or by DDLs. Snowflake only handles it in the event of major catastrophes.
Semi-structured data support
One of the most important steps toward big data is Snowflake’s capability to mix structured and semi-structured data without the usage of complicated technologies like Hadoop or Hive. Machine-generated data, sensors, and mobile devices are just a few of the sources of data. Using the VARIANT data type, which has a maximum limit of 16MB, Snowflake enables semi-structured data intake in a number of formats, including JSON, Avro, ORC, Parquet, and XML.
By extracting as much of the data in columnar format as feasible and storing the remaining portions in a single column, Snowflake also optimizes the data. Nested structures in semi-structured data can be flattened using data functions that can analyze, extract, cast, and alter data.
Constant Information Pipelines
Continuous data pipelines automate many of the manual processes required in loading data into Snowflake tables and subsequently transforming the data for additional analysis. In order to develop continuous data pipeline workflows, Snowflake includes a number of capabilities that enable continuous data ingestion, change data tracking, and the creation of repeating tasks.