This topic provides an overview of the main options available for loading data into Snowflake.
Supported file locations¶
Snowflake refers to the location of data files in cloud storage as ascenario. HeCOPY TO <table>The command used for bulk and continuous data upload (i.e. Snowpipe) supports cloud storage accounts managed by your business unit (i.e.external internships) and cloud storage included with your Snowflake account (i.e.internal stages).
external internships¶
Uploading data from any of the following cloud storage services is supported, regardless ofcloud platformHost your Snowflake account:
Amazonas S3
Google Cloud Storage
Microsoft Azure
You cannot access data stored in archived cloud storage classes that must be restored before they can be retrieved. These archive storage classes include, for example, the Amazon S3 Glacier Flexible Retrieval or Glacier Deep Archive storage class, or Microsoft Azure Archive Storage.
upload (ex.scenario) to your cloud storage account using the tools provided by the cloud storage service.
A named external stage is a database object created in a schema. This object stores the URL of the files in the cloud storage, the settings used to access the cloud storage account, and practical settings such as options describing the format of the provided files. Create scenarios withCREATE SCENARIODomain.
to use
Uploading file data to a cloud storage service on a different cloud platform or region than your Snowflake account may incur some data transfer charges. For more information, seeUnderstanding data transfer costs.
internal stages¶
Snowflake maintains the following types of stages in your account:
- from the user
Each user is assigned a user level for storing files. This type of staging is designed to store files provided and managed by a single user, but which can be loaded into multiple tables. User levels cannot be changed or deleted.
- Tisch
There is one table level available for each table created in Snowflake. This type of staging is designed to store files that are provided and maintained by one or more users, but are only loaded into a single table. Table levels cannot be modified or deleted.
Note that a table level is not a separate database object; instead, it is an implicit phase linked to the table itself. A desktop stage has no grantable rights of its own. To publish files in a table scenario, list the files, reference them in the scenario, or delete them, you must be the owner of the table (have the role with OWNERSHIP privileges on the table).
- Called
A named internal stage is a database object created in a schema. This type of staging can store files provided and managed by one or more users and loaded into one or more tables. Since named stages are database objects, the ability to create, modify, use, or delete them can be controlled by security access control privileges. Create scenarios withCREATE SCENARIODomain.
Upload files from your local file system using one of the internal storage typesTO PLACEDomain.
Bulk cargo versus continuous cargo¶
Snowflake offers the following main data loading solutions. The best solution may depend on the amount of data to upload and the upload frequency.
Bulk upload with the COPY command¶
This option allows you to batch upload data from files already available on cloud storage or copy (eg.staged) from a local machine to an internal cloud storage location (e.g. Snowflake) before loading the data into tables using the COPY command.
computing resources¶
Bulk loading relies on user-supplied virtual stores specified in the COPY statement. Users must size the bearing properly to accommodate expected loads.
Simple transformations during a loading process¶
Snowflake supports transforming data when it is loaded into a table using the COPY command. Options include:
column reorganization
skip column
Activities
Truncate text strings that exceed the target column length
Your data files don't need to have the same number and order of columns as your target table.
Continuous Loading with Snowpipe¶
This option is designed to load small amounts of data (eg micro-batches) and gradually make them available for analysis. Snowpipe loads data minutes after files are added to a stage and sent for processing. This ensures users have the latest results as soon as raw data is available.
computing resources¶
Snowpipe uses compute resources provided by Snowflake (ie a serverless computing model). These Snowflake-provided resources automatically scale and grow or shrink as needed, and are billed on a per-second basis. Data ingestion is calculated based on actual workload.
Simple transformations during a loading process¶
The COPY statement in a pipeline definition supports the same COPY conversion options as bulk data loading.
Additionally, data pipelines can leverage Snowpipe to continuously load micro-batch data into staging tables for transformation and optimization using automated tasks and change data capture (CDC) information in streams.
Data pipelines for complex transformations¶
Adata pipelinelets you apply complex transformations to loaded data. This workflow typically uses Snowpipe to load "raw" data into a staging table, then uses a series of table flows and tasks to transform and refine the new data for analysis.
Loading topic data from Apache Kafka¶
HeSnowflake Connector for Kafkaallows users to connect to aapache kafkaServer, read data from one or more topics and load that data into Snowflake tables.
Recognition of column definitions in layered semi-structured data files¶
Semi-structured data can contain thousands of columns. Snowflake offers robust solutions to handle this data. Options include referencing data directly from cloud storage using external tables, loading data into a single VARIANT column, or transforming and loading data into separate columns in a standard relational table. All of these options require some knowledge of column definitions in the data.
Another solution is to automatically detect the schema in a layered set of semi-structured data files and retrieve the column definitions. Column definitions include the names, data types, and order of columns in the files. Generate syntax in a format suitable for creating standard Snowflake tables, external tables, or views.
to use
This feature is currently limited to Apache Parquet, Apache Avro, and ORC files.
This support is implemented through the following SQL functions:
- INFER_SCHEMA
Recognizes column definitions in a set of given data files and retrieves the metadata in a format suitable for creating Snowflake objects.
- GENERATE_COLUMN_DESCRIPTION
Generates a list of columns from a given set of files using the output of the INFER_SCHEMA function.
These SQL functions support inner and outer stages.
Create tables or external tables with column definitions derived from a set of staging files using theCREATE TABLE... WITH MODELoCREATE EXTERNAL TABLE... WITH MODELSyntax. The USING TEMPLATE clause accepts an expression that calls the INFER_SCHEMA SQL function to discover the column definitions in the files. Once the table is created, you can use a COPY statement with theMATCH_BY_COLUMN_NAMEAbility to upload files directly into structured table.
Alternatives for loading data¶
You can use the following option to query your data from cloud storage without loading it into Snowflake tables.
External Tables (Data Lake)¶
external tablesEnable querying existing data stored on external cloud storage for analysis without first loading it into Snowflake. The source of data veracity remains in the external cloud storage. Records materialized in Snowflake using materialized views are read-only.
This solution is particularly beneficial for accounts that have stored a large amount of data on external cloud storage and only want to view a portion of the data; for example, the most recent data. Users can create materialized views on subsets of this data to improve query performance.
Working with Amazon S3 Compatible Storage¶
You can create stages and external tables in software and devices, on premises or in a private cloud that is highly supported by the Amazon S3 API. This feature makes it easier and more efficient to manage, control and analyze your data, regardless of where it is physically stored. For more details seeWorking with Amazon S3 Compatible Storage.
FAQs
What is the difference between Snowflake parquet and CSV? ›
Parquet is column oriented and CSV is row oriented. Row-oriented formats are optimized for OLTP workloads while column-oriented formats are better suited for analytical workloads.
What are the Snowflake rules? ›Do not plug any personal external drive into your Snowflake devices. Be paranoid in public. Don't work on a confidential presentation on a train or have a sensitive conversation while you're waiting in line at your local coffee shop. Don't modify or disable passwords or other security and safety features.
What is Snowflake overview? ›What is Snowflake? Developed in 2012, Snowflake is a fully managed SaaS (software as a service) that provides a single platform for data warehousing, data lakes, data engineering, data science, data application development, and secure sharing and consumption of real-time / shared data.
What are the four types of Snowflake tables? ›Snowflake offers three types of tables namely, Temporary, Transient & Permanent.
Is Parquet a JSON? ›Unlike CSV and JSON, Parquet files are binary files that contain meta data about their contents, so without needing to read/parse the content of the file(s), Spark can just rely on the header/meta data inherent to Parquet to determine column names and data types.
What are the six workloads of Snowflake? ›- Snowflake Workloads Overview.
- Data Applications.
- Data Engineering.
- Data Marketplace.
- Data Science.
- Data Warehousing.
- Marketing Analytics.
- Unistore.
What Data Can Be Shared in Snowflake? In Snowflake, you can configure your account to share tables (standard and external), secure views (standard and materialized) and secure User Defined Functions (UDFs).
What is the difference between Snowflake and snowflake schema? ›It is called snowflake because its diagram resembles a Snowflake. In a star schema, only single join defines the relationship between the fact table and any dimension tables. Star schema contains a fact table surrounded by dimension tables. A snowflake schema requires many joins to fetch the data.
What are the 7 main shapes of a snowflake? ›This system defines the seven principal snow crystal types as plates, stellar crystals, columns, needles, spatial dendrites, capped columns, and irregular forms.
Why is a snowflake 6 sided? ›All snowflakes contain six sides or points owing to the way in which they form. The molecules in ice crystals join to one another in a hexagonal structure, an arrangement which allows water molecules - each with one oxygen and two hydrogen atoms - to form together in the most efficient way.
What does snowflake do for dummies? ›
Snowflake is an elastically scalable cloud data warehouse
Snowflake is a cloud data warehouse that can store and analyze all your data records in one place. It can automatically scale up/down its compute resources to load, integrate, and analyze data.
Snowflake supports both ETL and ELT and works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion and others.
Why Snowflake is better than AWS? ›Snowflake implements instantaneous auto-scaling while Redshift requires addition/removal of nodes for scaling. Snowflake supports fewer data customization choices, where Redshift supports data flexibility through features like partitioning and distribution.
Is Snowflake a data warehouse or data lake? ›Snowflake Has Always Been a Hybrid of Data Warehouse and Data Lake. There's a great deal of controversy in the industry these days around data lakes versus data warehouses. For many years, a data warehouse was the only game in town for enterprises to process their data and get insight from it.
What type of SQL is used in Snowflake? ›How is It Supported in Snowflake? Snowflake is a data platform and data warehouse that supports the most common standardized version of SQL: ANSI. This means that all of the most common operations are usable within Snowflake.
What type of schema is Snowflake? ›A snowflake schema is a multi-dimensional data model that is an extension of a star schema, where dimension tables are broken down into subdimensions. Snowflake schemas are commonly used for business intelligence and reporting in OLAP data warehouses, data marts, and relational databases.
How is Snowflake different from SQL? ›MS SQL data warehousing server processes all share the same pool of compute resources. Snowflake allows you to segregate use cases into their own compute buckets, improving performance and managing cost.
Why use Parquet instead of CSV? ›Parquet with “gzip” compression (for storage): It is slightly faster to export than just . csv (if the CSV needs to be zipped, then parquet is much faster). Importing is about 2x times faster than CSV. The compression is around 22% of the original file size, which is about the same as zipped CSV files.
Is Parquet a CSV? ›Although it may seem obvious, parquet files have a . parquet extension and unlike a CSV, it is not a plain text file (it is represented in binary form), which means that we cannot open and examine it with a simple text editor. The parquet format is a type of column-oriented file format.
What is an alternative to Parquet? ›- Azure Cosmos DB.
- Google Cloud BigQuery.
- Snowflake.
- MariaDB.
- Amazon Redshift.
- Google Cloud BigTable.
- Vertica.
- Azure Table Storage.
What is SLA in Snowflake? ›
Summary. Snowflake is adding a 99.99% SLA target to its service-level agreement (SLA). Our data shows that our existing SLA, targeting 99.9%, proved to actually be better for customers, so we're also keeping that SLA.
What are the three Snowflake stage types? ›- External stages are storage locations outside the Snowflake environment in another cloud storage location. ...
- User stages are personal storage locations for each user. ...
- Table stages are storage locations held within a table object.
- Summary.
- Numeric.
- String & Binary.
- Logical.
- Date & Time.
- Semi-Structured.
- Geospatial.
- Unsupported.
- MongoDB Atlas.
- Oracle Database.
- Amazon Redshift.
- Redis Enterprise Cloud.
- DataStax Enterprise.
- Db2.
- CDP Data Hub.
- Couchbase Server.
What is a data-processing agreement? A data processing agreement is very similar to a data sharing agreement, but this is an agreement issued by a Controller to a data Processor. If your organisation is subject to the GDPR, you must have a written data processing agreement in place with all your data processors.
Is Snowflake SAAS or PaaS? ›Snowflake Data Cloud allows you to run all your critical data workloads on one platform, including data sharing, data lake, data warehouse,, and custom development capabilities, in effect also serving as a data PaaS.
What is better than a Snowflake? ›The usual alternatives to Snowflake are Amazon Redshift, Microsoft SQL Server, Azure Synapse Analytics, and Google's BigQuery. Alternatively, if you are looking for other cloud data platforms similar to Snowflake, here are seven to consider.
Is Snowflake relational or Nosql? ›Snowflake is a cloud-hosted relational database for building data warehouses.
Does Snowflake use SQL or Nosql? ›Snowflake is fundamentally built to be a complete SQL database. It is a columnar-stored relational database and works well with Tableau, Excel and many other tools familiar to end users.
Why is snowflake called snowflake? ›Origins of the allegoric meaning
It is popularly believed that every snowflake has a unique structure. Most usages of "snowflake" make reference to the physical qualities of snowflakes, such as their unique structure or fragility, while a minority of usages make reference to the white color of snow.
What is the life cycle of a snowflake? ›
It begins with a speck. The speck comes from dust or pollen floating in a cloud. The droplet freezes into a ball of ice. More water vapor sticks to the ball of ice and it grows into an ice crystal.
What are the lines on a snowflake called? ›The six "arms" of the snowflake, or dendrites, then grow independently from each of the corners of the hexagon, while either side of each arm grows independently.
Why is each snowflake unique? ›Because a snowflake's shape evolves as it journeys through the air, no two will ever be the same. Even two flakes floating side by side will each be blown through different levels of humidity and vapour to create a shape that is truly unique.
Why is Snowflake so popular? ›Snowflake is the most popular solution, supporting multi-cloud infrastructure environments such as Amazon, Microsoft, and GCP. It's a highly scalable cloud data warehouse “as-a-service” enabling users to focus on analyzing data rather than spending time managing and tuning. .
Is Snowflake difficult to learn? ›Things are different with Snowflake since it is fully SQL-based. Chances are, you have some experience using BI or data analysis tools that work on SQL. Most of what you already know can be applied to Snowflake. Not to mention that SQL is an easy-to-learn language, a significant benefit for general users.
Why Snowflake is better than Azure? ›Snowflake offers native connectivity to multiple BI, data integration, and analytics tools . Azure comes with integration tools such as logic apps, API Management, Service Bus, and Event Grid for connecting to third-party services. Both the user and AWS are responsible for securing data.
Which ETL tool is in demand in 2022? ›Matillion. Matillion is one of the younger, cloud-based ETL solutions on the market. It consists of three components: the underlying platform, a graphical data orchestration tool, and a management tool.
Is Snowflake on AWS or Azure? ›A Snowflake account can be hosted on any of the following cloud platforms: Amazon Web Services (AWS) Google Cloud Platform (GCP) Microsoft Azure (Azure)
What are real life examples of ETL? ›Fraud detection, Internet of Things, edge computing, streaming analytics, and real-time payment processing are examples of applications that rely on streaming ETL. Streaming ETL: Real-time applications require streaming ETL.
What are the disadvantages of Snowflake? ›- No support for unstructured data at the moment. Snowflake currently only caters to structured and semi-structured data. ...
- Only bulk data load. When migrating data from data files to Snowflake files there is much support and guidance on bulk data loading. ...
- No data constraints.
Which company owns Snowflake? ›
...
Snowflake Inc.
| Type | Public company |
|---|---|
| Founded | July 23, 2012 |
| Founders | Benoît Dageville Thierry Cruanes Marcin Żukowski |
| Headquarters | Bozeman, Montana, U.S. |
| Key people | Frank Slootman, Chairperson & CEO Benoît Dageville, President Thierry Cruanes, CTO |
When assessing the two solutions, reviewers found Snowflake easier to use and do business with overall. However, reviewers preferred the ease of set up with Azure Data Lake Store, along with administration. Reviewers felt that Azure Data Lake Store meets the needs of their business better than Snowflake.
Is Hadoop a data lake or data warehouse? ›Hadoop is an important element of the architecture that is used to build data lakes. A Hadoop data lake is one which has been built on a platform made up of Hadoop clusters. Hadoop is particularly popular in data lake architecture as it is open source (as part of the Apache Software Foundation project).
What is the difference between OLAP and OLTP? ›OLTP and OLAP: The two terms look similar but refer to different kinds of systems. Online transaction processing (OLTP) captures, stores, and processes data from transactions in real time. Online analytical processing (OLAP) uses complex queries to analyze aggregated historical data from OLTP systems.
Can Snowflake store JSON data? ›In Snowflake, you can natively ingest semi-structured data not only in JSON but also in XML, Parquet, Avro, ORC, and other formats. This means that in Snowflake, you can efficiently store JSON data and then access it using SQL. Snowflake JSON allows you to load JSON data directly into relational tables.
What is the difference between Parquet and CSV? ›Parquet with “gzip” compression (for storage): It is slightly faster to export than just . csv (if the CSV needs to be zipped, then parquet is much faster). Importing is about 2x times faster than CSV. The compression is around 22% of the original file size, which is about the same as zipped CSV files.
Are Parquet files better than CSV files? ›With the parquet file format, the team was able to process data 1,500 times faster than with CSVs.
What is the difference between Parquet and CSV in s3? ›In a CSV file (remember, row-oriented) each record is a row. In Parquet, however, it is each column that is stored independently. The most extreme difference is noticed when, in a CSV file, we want to read only one column.
Which is better JSON or CSV? ›JSON is referred to as comparatively better than CSV while working with the large volume of data and in terms of scalability of files or application. CSV is excellent at working with small files and fewer data.
What are the three types of CSV? ›- The Three Levels of CSV.
- Reconceiving Products & Markets.
- Redefining Productivity in the Value Chain.
- Improving the Local & Regional Business Environment.
How is JSON different from CSV? ›
json. JSON is known as a light-weight data format type and is favored for its human readability and nesting features. It is often used in conjunction with APIs and data configuration. CSV: CSV is a data storage format that stands for Comma Separated Values with the extension .
Can Excel read Parquet files? ›The Parquet Excel Add-In is a powerful tool that allows you to connect with live Parquet data, directly from Microsoft Excel. Use Excel to read, write, and update Parquet data files.
How can you tell if a file is Parquet? ›You can use Hadoop FileSystem API. To check if the directory contains Parquet on CSV files, you can use listStatus method to list the files under that directory, and for each file, you can check its extension to determine its type ( . csv or . parquet ).
Is Parquet better than JSON? ›Parquet is one of the fastest file types to read generally and much faster than either JSON or CSV.
Can you convert Parquet to CSV? ›Convert Parquet to CSV
We can now write our multiple Parquet files out to a single CSV file using the to_csv method. Make sure to set single_file to True and index to False. Let's verify that this actually worked by reading the csv file into a pandas DataFrame.
SELECT from a parquet file using OPENROWSET
SQL Server will read the schema from the file itself, so there is no need to define the table, columns, or data types. There is no need to declare the type of compression for the file to be read.
Snowflake supports both ETL and ELT and works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion and others.