BohuTANG commented 1 year ago

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	Yes
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 \| TRAILING} [remstr] FROM] str)`	`TRIM([{BOTH \| LEADING \| TRAILING} [remstr] FROM] str)`
`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 \| FALSE ] [ , MODE => 'OBJECT' \| 'ARRAY' \| 'BOTH' ])`	`FLATTEN(INPUT => expr [, PATH => expr ] [ , OUTER => TRUE \| FALSE ] [ , RECURSIVE => TRUE \| FALSE ] [ , MODE => 'OBJECT' \| 'ARRAY' \| 'BOTH' ])`
`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)`

ZhiHanZ commented 1 year ago

Feature	Snowflake	Databend
Kafka Connect	Yes	Yes
Flink CDC	No	Yes
dbt	Yes	Yes
go driver	Yes	Yes
java driver	Yes	Yes
rust driver	No	Yes
python driver	Yes	Yes
nodejs driver	Yes	Planning
embedded python lib	No	Yes
Iceberg	Private Preview	Yes

PsiACE commented 1 year ago

we hava a nodejs driver, see:

In addition, although we do not yet support geospatial types, we have already implemented a wide range of geospatial functions. I believe this is worth mentioning.

https://databend.rs/doc/sql-functions/geo-functions/

I think, in Storage Format, perhaps we can also add our native format and indicate that it can bring better performance.

xudong963 commented 11 months ago

We also support aggregate and hash join spilling.

Chasen-Zhang commented 9 months ago

@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 commented 9 months ago

@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 commented 1 day ago

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 commented 23 hours ago

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 commented 14 hours ago

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.

databendlabs / databend

Databend vs. Snowflake Features and Capabilities #13059