Databend vs. Snowflake Features and Capabilities #13059

BohuTANG · 2023-09-27T09:10:22Z

Features and Capabilities

Databend is an open-source alternative to Snowflake, so we need a page to introduce the differences between Databend and Snowflake.
Feel free to contribute if you notice any omissions.

	Category	Snowflake	Databend
Numeric Data Types	Data Types	Yes	Yes [1, 2]
String & Binary Data Types	Data Types	Yes	Yes
Logical Data Types	Data Types	Yes	Yes
Date & Time Data Types	Data Types	Yes	Yes
Geospatial Data Types	Data Types	Yes	Yes
Bitmap Data Types	Data Types	No	Yes
Semi-structured Data Types	Data Types	Yes	Yes
Database	Databases	Yes	Yes
Sequence	Sequences	Yes	Yes
Table	Tables	Yes	Yes
External Location Table(Store data in your own S3 bucket)	Tables	No (Snowflake Native Format with Micro-Partition)	Yes (Databend Native Format with FUSE Table Format)
Vacuum	Tables	No	Yes
Clustering	Tables	Yes	Yes(Distributed)
Full-Text Index	Tables	No	Yes
Hive	Lake	Yes	Yes
Iceberg	Lake	Yes	Yes
View	Views	Yes	Yes
Materialized Views	Views	Yes	Yes
User, Role, & Privilege	Privileges	Yes	Yes
Query	Queries	Yes	Yes
Update	General DML	Yes	Yes(Distributed)
Replace	General DML	No	Yes(Distributed)
Delete	General DML	Yes	Yes(Distributed)
Merge	General DML	Yes	Yes
Stage(Internal/External/Remove/List)	Data Loading	Yes	Yes
Query Stage File	Data Loading	Yes	Yes
Copy Into Table/Location	Data Loading	Yes	Yes(Distributed)
Transforming Data During a Load	Data Loading	Yes	Yes
Pipe	Data Loading	Yes	Yes(Cloud)
Task	Data Loading	Yes, minimum schedule is 1 minute	Yes, minimum schedule is 500 millisecond
Stream	Data Loading	Yes	Yes
Table Stream(Append-only/Standard)	Data Loading	Yes	Yes
User-Defined Functions	Functions	Yes	Yes
External Functions	Functions	Yes	Yes
Stored Procedure	Functions	Yes	Planning
AI Functions	Functions	Yes, 2024 Add	Yes
Window Function	Functions	Yes	Yes
Time Travel	Data Recovery	Yes	Yes
Data Sharing	Data Sharing	Yes	Yes
RBAC	Security	Yes	Yes
Network Policies	Security	Yes	Yes
Masking Policies	Security	Yes	Yes
Kafka Connect	Connectors	Yes	Yes
Flink CDC	Connectors	No	Yes
dbt	Connectors	Yes	Yes
Drivers	Drivers	Go Java Ptyhon Node.js	Go Java Python Node.js Rust
Embedded as a Python Library		No	Yes
Storage Format	Storage	MicroPartition	Parquet
Aggregate&Join&Sort Spill	Performance Optimization	Yes	Yes
Local Disk Cache	Performance Optimization	Yes	Yes
Profiling	Performance Optimization	Powerful	Powerful
CLI Client	Client	SnowSQL	BendSQL
TPC-H	Benchmark	Yes	Yes
TPC-DS	Benchmark	Yes	Yes
Supported Cloud Platforms	Cloud	AWS/GCP/AZURE	Any Cloud

Databend and Snowflake Functions Comparison

Mathematical Functions

Databend Function	Snowflake Function
`ABS(x)`	`ABS(x)`
`ACOS(x)`	`ACOS(x)`
`ASIN(x)`	`ASIN(x)`
`ATAN(x)`	`ATAN(x)`
`ATAN2(y, x)`	`ATAN2(y, x)`
`CEIL(x)`	`CEIL(x)`
`CEILING(x)`	`CEILING(x)`
`COS(x)`	`COS(x)`
`DEGREES(x)`	`DEGREES(x)`
`EXP(x)`	`EXP(x)`
`FLOOR(x)`	`FLOOR(x)`
`LN(x)`	`LN(x)`
`LOG(x)`	`LOG(x)`
`LOG10(x)`	`LOG10(x)`
`LOG2(x)`	`LOG2(x)`
`PI()`	`PI()`
`POW(x, y)`	`POW(x, y)`
`POWER(x, y)`	`POWER(x, y)`
`RADIANS(x)`	`RADIANS(x)`
`RAND()`	`RAND()`
`ROUND(x, d)`	`ROUND(x, d)`
`SIGN(x)`	`SIGN(x)`
`SIN(x)`	`SIN(x)`
`SQRT(x)`	`SQRT(x)`
`TAN(x)`	`TAN(x)`

String Functions

Databend Function	Snowflake Function
`ASCII(expr)`	`ASCII(expr)`
`CHAR(n, ...)`	`CHAR(n, ...)`
`CONCAT(expr1, ...)`	`CONCAT(expr1, ...)`
`CONCAT_WS(separator, expr1, ...)`	`CONCAT_WS(separator, expr1, ...)`
`INSTR(str, substr)`	`INSTR(str, substr)`
`LENGTH(str)`	`LENGTH(str)`
`LCASE(str)`	`LOWER(str)`
`LEFT(str, len)`	`LEFT(str, len)`
`LOWER(str)`	`LOWER(str)`
`LTRIM(str)`	`LTRIM(str)`
`POSITION(substr IN str)`	`POSITION(substr IN str)`
`REPEAT(str, count)`	`REPEAT(str, count)`
`REPLACE(str, from_str, to_str)`	`REPLACE(str, from_str, to_str)`
`REVERSE(str)`	`REVERSE(str)`
`RIGHT(str, len)`	`RIGHT(str, len)`
`RTRIM(str)`	`RTRIM(str)`
`SPACE(n)`	`SPACE(n)`
`SUBSTR(str, pos)`	`SUBSTR(str, pos)`
`SUBSTR(str, pos, len)`	`SUBSTR(str, pos, len)`
`SUBSTRING(str, pos)`	`SUBSTRING(str, pos)`
`SUBSTRING(str, pos, len)`	`SUBSTRING(str, pos, len)`
`TRIM([{BOTH	LEADING
`UCASE(str)`	`UPPER(str)`
`UPPER(str)`	`UPPER(str)`

Date and Time Functions

Databend Function	Snowflake Function
`ADD_DAYS(date, interval)`	`DATEADD(day, interval, date)`
`ADD_HOURS(date, interval)`	`DATEADD(hour, interval, date)`
`ADD_MINUTES(date, interval)`	`DATEADD(minute, interval, date)`
`ADD_MONTHS(date, interval)`	`DATEADD(month, interval, date)`
`ADD_SECONDS(date, interval)`	`DATEADD(second, interval, date)`
`ADD_YEARS(date, interval)`	`DATEADD(year, interval, date)`
`DATE(expr)`	`DATE(expr)`
`DAY(date)`	`DAY(date)`
`DAYNAME(date)`	`DAYNAME(date)`
`DAY_OF_MONTH(date)`	`DAYOFMONTH(date)`
`DAY_OF_WEEK(date)`	`DAYOFWEEK(date)`
`DAY_OF_YEAR(date)`	`DAYOFYEAR(date)`
`HOUR(date)`	`HOUR(date)`
`LAST_DAY(date)`	`LAST_DAY(date)`
`MINUTE(time)`	`MINUTE(time)`
`MONTH(date)`	`MONTH(date)`
`NOW()`	`CURRENT_TIMESTAMP()`
`SECOND(time)`	`SECOND(time)`
`TO_DATE(expr)`	`TO_DATE(expr)`
`TO_TIMESTAMP(expr)`	`TO_TIMESTAMP(expr)`
`WEEK(date)`	`WEEK(date)`
`YEAR(date)`	`YEAR(date)`
`YESTERDAY()`	`DATEADD(day, -1, CURRENT_DATE())`
`TOMORROW()`	`DATEADD(day, 1, CURRENT_DATE())`
`TO_START_OF_DAY(expr)`	`DATE_TRUNC('day', expr)`
`TO_START_OF_HOUR(expr)`	`DATE_TRUNC('hour', expr)`
`TO_START_OF_MINUTE(expr)`	`DATE_TRUNC('minute', expr)`
`TO_START_OF_MONTH(expr)`	`DATE_TRUNC('month', expr)`
`TO_START_OF_QUARTER(expr)`	`DATE_TRUNC('quarter', expr)`
`TO_START_OF_WEEK(expr)`	`DATE_TRUNC('week', expr)`
`TO_START_OF_YEAR(expr)`	`DATE_TRUNC('year', expr)`
`TO_UNIX_TIMESTAMP(expr)`	`TO_TIMESTAMP(expr)`
`TO_YEARMONTH(expr)`	`TO_CHAR(expr, 'YYYY-MM')`
`TO_YYYYMMDD(expr)`	`TO_CHAR(expr, 'YYYYMMDD')`
`TO_YYYYMMDDHH(expr)`	`TO_CHAR(expr, 'YYYYMMDDHH')`
`TO_YYYYMMDDHHMMSS(expr)`	`TO_CHAR(expr, 'YYYYMMDDHHMMSS')`

Aggregate Functions

Databend Function	Snowflake Function
`AVG(x)`	`AVG(x)`
`COUNT(x)`	`COUNT(x)`
`MAX(x)`	`MAX(x)`
`MIN(x)`	`MIN(x)`
`SUM(x)`	`SUM(x)`

Conversion Functions

Databend Function	Snowflake Function
`CAST(expr AS type)`	`CAST(expr AS type)`
`TO_BOOLEAN(expr)`	`CAST(expr AS BOOLEAN)`
`TO_STRING(expr)`	`CAST(expr AS STRING)`
`TO_INT32(expr)`	`CAST(expr AS INT)`
`TO_INT64(expr)`	`CAST(expr AS BIGINT)`
`TO_UINT32(expr)`	`CAST(expr AS NUMBER)`
`TO_UINT64(expr)`	`CAST(expr AS NUMBER)`
`TO_FLOAT32(expr)`	`CAST(expr AS FLOAT)`
`TO_FLOAT64(expr)`	`CAST(expr AS DOUBLE)`
`TO_DATE(expr)`	`CAST(expr AS DATE)`
`TO_TIMESTAMP(expr)`	`CAST(expr AS TIMESTAMP)`
`TRY_CAST(expr AS type)`	`TRY_CAST(expr AS type)`

JSON Functions

Databend Function	Snowflake Function
`JSON_OBJECT(key1, value1[, key2, value2[, ...]])`	`OBJECT_CONSTRUCT(key1, value1[, key2, value2[, ...]])`
`PARSE_JSON(expr)`	`PARSE_JSON(expr)`
`JSON_EXTRACT_PATH_TEXT(expr, path_name)`	`GET(expr, path_name)`
`JSON_PRETTY(json_string)`	`TO_JSON_STRING(json_string)`
`JSON_ARRAY(value1[, value2[, ...]])`	`ARRAY_CONSTRUCT(value1[, value2[, ...]])`
`JSON_PATH_QUERY(variant, path_name)`	`PATH(expr, path_name)`
`JSON_PATH_QUERY_FIRST(variant, path_name)`	`PATH(expr, path_name)`
`CHECK_JSON(expr)`	`IS_JSON(expr)`
`GET_PATH(variant, path_name)`	`GET_PATH(variant, path_name)`
`JSON_PATH_EXISTS(json_data, json_path_expression)`	`JSON_PATH_EXISTS(json_data, json_path_expression)`
`JSON_OBJECT_KEEP_NULL(key1, value1[, key2, value2[, ...]])`	`OBJECT_CONSTRUCT_KEEP_NULL(key1, value1[, key2, value2[, ...]])`
`IS_INTEGER(expr)`	`IS_INTEGER(expr)`
`IS_NULL_VALUE(expr)`	`IS_NULL_VALUE(expr)`
`IS_STRING(expr)`	`IS_STRING(expr)`
`IS_BOOLEAN(expr)`	`IS_BOOLEAN(expr)`
`FLATTEN(INPUT => expr [, PATH => expr ] [ , OUTER => TRUE	FALSE ] [ , RECURSIVE => TRUE
`IS_ARRAY(expr)`	`IS_ARRAY(expr)`
`GET(variant, index)`	`GET(variant, index)`
`JSON_PATH_QUERY_ARRAY(variant, path_name)`	`PATH_ARRAY(variant, path_name)`
`GET_IGNORE_CASE(variant, field_name)`	`GET_IGNORE_CASE(variant, field_name)`
`JSON_ARRAY_ELEMENTS(json_string)`	`ARRAY_ELEMENTS(json_string)`
`JSON_STRIP_NULLS(json_string)`	`REMOVE_NULLS(json_string)`
`AS_BOOLEAN(variant)`	`CAST(variant AS BOOLEAN)`
`AS_INTEGER(variant)`	`CAST(variant AS INTEGER)`
`AS_FLOAT(variant)`	`CAST(variant AS FLOAT)`
`AS_STRING(variant)`	`CAST(variant AS STRING)`
`AS_ARRAY(variant)`	`CAST(variant AS ARRAY)`
`AS_OBJECT(variant)`	`CAST(variant AS OBJECT)`

Bitmap Functions

Databend Function	Snowflake Function
`BUILD_BITMAP(expr)`	N/A
`TO_BITMAP(expr)`	N/A
`BITMAP_AND(expr1, expr2)`	N/A
`BITMAP_OR(expr1, expr2)`	N/A
`BITMAP_XOR(expr1, expr2)`	N/A
`BITMAP_HAS_ALL(expr1, expr2)`	N/A
`BITMAP_HAS_ANY(expr1, expr2)`	N/A
`BITMAP_TO_STRING(expr)`	N/A

Conditional Functions

Databend Function	Snowflake Function
`IF(cond, expr1, expr2)`	`IFF(cond, expr1, expr2)`
`NULLIF(expr1, expr2)`	`NULLIF(expr1, expr2)`
`IFNULL(expr1, expr2)`	`IFNULL(expr1, expr2)`
`COALESCE(expr1, expr2[, ...])`	`COALESCE(expr1, expr2[, ...])`

Encryption and Hash Functions

Databend Function	Snowflake Function
`MD5(expr)`	`MD5(expr)`
`SHA(expr)`	`SHA(expr)`
`SHA2(expr, x)`	`SHA2(expr, x)`
`CRC32(expr)`	`CRC32(expr)`
`XXHASH32(expr)`	N/A
`XXHASH64(expr)`	N/A
`BLAKE3(expr)`	N/A
`SIPHASH64(expr)`	N/A
`CITY64WITHSEED(expr1, expr2)`	N/A

Window Functions

Databend Function	Snowflake Function
`ROW_NUMBER()`	`ROW_NUMBER()`
`RANK()`	`RANK()`
`DENSE_RANK()`	`DENSE_RANK()`
`PERCENT_RANK()`	`PERCENT_RANK()`
`CUME_DIST()`	`CUME_DIST()`
`NTILE(n)`	`NTILE(n)`
`LAG(expr, n, default)`	`LAG(expr, n, default)`
`LEAD(expr, n, default)`	`LEAD(expr, n, default)`
`FIRST_VALUE(expr)`	`FIRST_VALUE(expr)`
`LAST_VALUE(expr)`	`LAST_VALUE(expr)`
`NTH_VALUE(expr, n)`	`NTH_VALUE(expr, n)`

Array Functions

Databend Function	Snowflake Function
`ARRAY_REDUCE(array, lambda)`	N/A
`ARRAY_FLATTEN(array)`	`ARRAY_FLATTEN( <array> )`
`SLICE(array, start[, end])`	`SLICE(array, start[, end])`
`ARRAY_TO_STRING(array, '<separator>')`	`ARRAY_TO_STRING(array, '<separator>')`
`CONTAINS(array, element)`	`ARRAY_CONTAINS(array, element)`
`ARRAY_FILTER(array, lambda)`	N/A
`ARRAY_SORT(array[, order[, nullposition]])`	`ARRAY_SORT(array[, order[, nullposition]])`
`UNNEST(array)`	`UNNEST(array)`
`ARRAY_CONCAT(array1, array2)`	`ARRAY_CAT(array1, array2)`
`GET(array, index)`	`GET(array, index)`
`ARRAY_INDEX_OF(array, element)`	`ARRAY_INDEX_OF(array, element)`
`ARRAY_UNIQUE(array)`	`ARRAY_UNIQUE(array)`
`ARRAY_REMOVE_FIRST(array)`	N/A
`ARRAY_TRANSFORM(array, lambda)`	`TRANSFORM( <array> , <lambda_expression> )`
`ARRAY_DISTINCT(array)`	`ARRAY_DISTINCT(array)`
`ARRAY_APPEND(array, element)`	`ARRAY_APPEND(array, element)`
`ARRAY_PREPEND(element, array)`	`ARRAY_PREPEND(element, array)`
`ARRAY_LENGTH(array)`	`ARRAY_LENGTH(array)`
`ARRAY_APPLY(array, lambda)`	N/A

Geospatial Functions

Databend Function	Snowflake Function
`H3_EXACT_EDGE_LENGTH_KM(h3)`	`H3_EXACT_EDGE_LENGTH_KM(h3)`
`H3_EDGE_LENGTH_M(1)`	`H3_EDGE_LENGTH_M(1)`
`H3_LINE(h3, a_h3)`	`H3_LINE(h3, a_h3)`
`H3_EDGE_LENGTH_KM(res)`	`H3_EDGE_LENGTH_KM(res)`
`H3_EXACT_EDGE_LENGTH_M(h3)`	`H3_EXACT_EDGE_LENGTH_M(h3)`
`GEOHASH_DECODE('<geohashed-string>')`	`GEOHASH_DECODE('<geohashed-string>')`
`H3_HEX_AREA_M2(res)`	`H3_HEX_AREA_M2(res)`
`H3_TO_GEO_BOUNDARY(h3)`	`H3_TO_GEO_BOUNDARY(h3)`
`H3_GET_UNIDIRECTIONAL_EDGES_FROM_HEXAGON(h3)`	`H3_GET_UNIDIRECTIONAL_EDGES_FROM_HEXAGON(h3)`
`H3_HEX_AREA_KM2(res)`	`H3_HEX_AREA_KM2(res)`
`H3_IS_VALID(h3)`	`H3_IS_VALID(h3)`
`H3_TO_GEO(h3)`	`H3_TO_GEO(h3)`
`H3_UNIDIRECTIONAL_EDGE_IS_VALID(h3)`	`H3_UNIDIRECTIONAL_EDGE_IS_VALID(h3)`
`GEO_TO_H3(lon, lat, res)`	`GEO_TO_H3(lon, lat, res)`
`GEOHASH_ENCODE(lon, lat)`	`GEOHASH_ENCODE(lon, lat)`
`POINT_IN_POLYGON((x,y), [(a,b), (c,d), (e,f) ... ])`	`POINT_IN_POLYGON((x,y), [(a,b), (c,d), (e,f) ... ])`
`H3_DISTANCE(h3, a_h3)`	`H3_DISTANCE(h3, a_h3)`
`H3_CELL_AREA_RADS2(h3)`	`H3_CELL_AREA_RADS2(h3)`
`H3_TO_CENTER_CHILD(h3, res)`	`H3_TO_CENTER_CHILD(h3, res)`
`H3_CELL_AREA_M2(h3)`	`H3_CELL_AREA_M2(h3)`
`H3_GET_FACES(h3)`	`H3_GET_FACES(h3)`
`H3_IS_PENTAGON(h3)`	`H3_IS_PENTAGON(h3)`
`STRING_TO_H3(h3)`	`STRING_TO_H3(h3)`
`H3_GET_UNIDIRECTIONAL_EDGE(h3, a_h3)`	`H3_GET_UNIDIRECTIONAL_EDGE(h3, a_h3)`
`H3_GET_BASE_CELL(h3)`	`H3_GET_BASE_CELL(h3)`
`H3_GET_UNIDIRECTIONAL_EDGE_BOUNDARY(h3)`	`H3_GET_UNIDIRECTIONAL_EDGE_BOUNDARY(h3)`
`H3_GET_DESTINATION_INDEX_FROM_UNIDIRECTIONAL_EDGE(h3)`	`H3_GET_DESTINATION_INDEX_FROM_UNIDIRECTIONAL_EDGE(h3)`
`H3_TO_PARENT(h3, parent_res)`	`H3_TO_PARENT(h3, parent_res)`
`H3_NUM_HEXAGONS(res)`	`H3_NUM_HEXAGONS(res)`
`H3_TO_CHILDREN(h3, child_res)`	`H3_TO_CHILDREN(h3, child_res)`
`H3_GET_INDEXES_FROM_UNIDIRECTIONAL_EDGE(h3)`	`H3_GET_INDEXES_FROM_UNIDIRECTIONAL_EDGE(h3)`
`H3_TO_STRING(h3)`	`H3_TO_STRING(h3)`
`H3_K_RING(h3, k)`	`H3_K_RING(h3, k)`
`H3_IS_RES_CLASS_III(h3)`	`H3_IS_RES_CLASS_III(h3)`
`H3_INDEXES_ARE_NEIGHBORS(h3, a_h3)`	`H3_INDEXES_ARE_NEIGHBORS(h3, a_h3)`
`H3_GET_ORIGIN_INDEX_FROM_UNIDIRECTIONAL_EDGE(h3)`	`H3_GET_ORIGIN_INDEX_FROM_UNIDIRECTIONAL_EDGE(h3)`
`H3_GET_RESOLUTION(h3)`	`H3_GET_RESOLUTION(h3)`

Other Functions

Databend Function	Snowflake Function
`NEXTVAL(seq_name)`	`NEXTVAL(seq_name)`
`QUOTE(str)`	`QUOTE(str)`

The text was updated successfully, but these errors were encountered:

xudong963 · 2023-10-08T04:31:41Z

We also support aggregate and hash join spilling.

Chasen-Zhang · 2023-12-04T08:38:07Z

@BohuTANG Could you help me change the address of the connection，
Here are the rules：
/doc/sql-reference/* => /sql/sql-reference/*
/doc/sql-commands/* => /sql/sql-commands/*
/doc/sql-functions/* =>/sql/sql-functions/*
(👆 Although I redirected all of these, it's better to change them all together. And you can directly change the domain name to https://docs.databend.com )

https://docs.databend.com/load-data/pipeline => https://docs.databend.com/doc/load-data/pipeline
https://databend.rs/blog/2023-08-01-iceberg-integration=> https://www.databend.com/blog/2023-08-01-iceberg-integration/

Thank you in advance！

cdmikechen · 2023-12-22T14:06:04Z

@BohuTANG
Wish to support Geospatial Data Types, we used to have a lot of spatial data needs, and if databend can support geo, it will be a great help to us ~

sfc-gh-space · 2024-09-27T20:44:49Z

I didn't do a comprehensive review, but your Snowflake features are out of date. A few examples:

Snowflake supports external tables, both in your own VPC and on-prem from S3 compatible storage arrays.
(https://docs.snowflake.com/en/user-guide/tables-external, https://docs.snowflake.com/en/user-guide/data-load-s3-compatible-storage)
Apache Iceberg™ is a trademarked term, and Snowflake supports them as a first party object. This feature is GA.
(https://docs.snowflake.com/en/user-guide/tables-iceberg)
Embedded Python Library: Snowflake natively runs Python and hosts thousands of Python libraries via Anaconda or you can upload your own libraries.
(https://docs.snowflake.com/en/developer-guide/snowpark/python/index, https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages)
Storage Format: Snowflake has three native storage formats: FDN (micro-partitions), Apache Iceberg™, and hybrid tables (for OLTP use cases).
(https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions, https://docs.snowflake.com/en/user-guide/tables-iceberg, https://docs.snowflake.com/en/user-guide/tables-hybrid-create)
Command Line: SnowSQL and Snowflake CLI.
(https://docs.snowflake.com/en/user-guide/snowsql, https://docs.snowflake.com/en/developer-guide/snowflake-cli-v2/index)
ARRAY_FLATTEN (https://docs.snowflake.com/en/sql-reference/functions/array_flatten)
ARRAY_TRANSFORM -> TRANSFORM (https://docs.snowflake.com/en/sql-reference/functions/transform)
Under Drivers you misspelled Python and Snowflake has in addition other drivers including .NET and PHP PDO. (https://docs.snowflake.com/en/developer-guide/drivers)

BohuTANG · 2024-09-28T04:11:57Z

Hi @sfc-gh-space,

Thank you for the information. I've updated the outdated sections with the information you provided.

Snowflake supports external tables, both in your own VPC and on-prem from S3-compatible storage arrays.

While Snowflake supports external tables, it's important to note that Databend’s external location tables operate differently (see: Databend External Location Tables). In Snowflake, you can't place the Snowflake-specific micro-partition table format into your own S3 bucket, as it's confined to Snowflake-managed storage. However, Databend allows external table data to be stored directly in your S3 bucket. Databend’s approach is more akin to Snowflake's external stage queries (see: Querying an External Stage).

Embedded Python Library:...

Here we have changed it to Embedded as a Python Library.

sfc-gh-space · 2024-09-28T13:20:03Z

Regarding your comment: "Databend allows external table data to be stored directly in your S3 bucket." Apache Iceberg™ is a fully native table format (e.g a first party object) for Snowflake since June 2024. This means Snowflake customers can choose to store their data in their own S3 bucket if they like. Apache Iceberg™ tables are Parquet files with metadata. Databend appears to store data in Parquet as well. I see zero distinction.

BohuTANG · 2024-09-29T04:01:46Z

Hi @sfc-gh-space,

Just to clarify, Databend's FUSE table format is our default format and is similar to Snowflake's table format with micro-partitions. It is optimized for performance, multi-transaction support, streaming capabilities, and includes significant improvements for columnar storage.

One key difference is that Databend allows you to store FUSE tables in external locations, such as your own bucket, while Snowflake's external tables only support external formats.

sfc-gh-space · 2024-09-30T13:23:39Z

I assume this purpose of this matrix is to help companies understand the difference between Databend and Snowflake. In this case, you've used a term ("external table") that means something specific to Snowflake which is causing confusion. You've attempted to clarify this by redefining external table to mean "native table in a customer's VPC". But here's the issue:

12 years ago Snowflake only had one internal table format, FDN. However, in 2024, Snowflake has additional native tables formats for different use cases. FDN (micro-partitions), Apache Iceberg™ (Parquet + metadata), hybrid tables (row+column), Event Tables (OpenTelemetry), etc.

If you have redefined this column to mean "native table format in customer VPC", then the answer for Snowflake is Yes: Apache Iceberg™ (Parquet + metadata). The answer for Databend is Yes: FUSE (Parquet + metadata).

Apache Iceberg™ tables in Snowflake are fully supported first party objects that use the same query compiler, have the same ability to prune, and support other native Snowflake features like live sharing, data governance, etc. More importantly for customers, performance on these tables are excellent.

BohuTANG pinned this issue Sep 27, 2023

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BohuTANG changed the title ~~doc: Snowflake & Databend~~ Comprehensive Comparison: Databend vs. Snowflake Features and Capabilities Oct 8, 2023

BohuTANG changed the title ~~Comprehensive Comparison: Databend vs. Snowflake Features and Capabilities~~ Databend vs. Snowflake Features and Capabilities Oct 8, 2023

BohuTANG unpinned this issue Dec 27, 2023

BohuTANG pinned this issue Dec 27, 2023

Xuanwo mentioned this issue Jun 17, 2024

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Databend vs. Snowflake Features and Capabilities #13059

Databend vs. Snowflake Features and Capabilities #13059

BohuTANG commented Sep 27, 2023 •

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xudong963 commented Oct 8, 2023

Chasen-Zhang commented Dec 4, 2023 •

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cdmikechen commented Dec 22, 2023

sfc-gh-space commented Sep 27, 2024

BohuTANG commented Sep 28, 2024

sfc-gh-space commented Sep 28, 2024

BohuTANG commented Sep 29, 2024

sfc-gh-space commented Sep 30, 2024

Databend vs. Snowflake Features and Capabilities #13059

Databend vs. Snowflake Features and Capabilities #13059

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BohuTANG commented Sep 27, 2023 • edited Loading

Features and Capabilities

Databend and Snowflake Functions Comparison

Mathematical Functions

String Functions

Date and Time Functions

Aggregate Functions

Conversion Functions

JSON Functions

Bitmap Functions

Conditional Functions

Encryption and Hash Functions

Window Functions

Array Functions

Geospatial Functions

Other Functions

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xudong963 commented Oct 8, 2023

Chasen-Zhang commented Dec 4, 2023 • edited Loading

cdmikechen commented Dec 22, 2023

sfc-gh-space commented Sep 27, 2024

BohuTANG commented Sep 28, 2024

sfc-gh-space commented Sep 28, 2024

BohuTANG commented Sep 29, 2024

sfc-gh-space commented Sep 30, 2024

BohuTANG commented Sep 27, 2023 •

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Chasen-Zhang commented Dec 4, 2023 •

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