自从Databricks提出Lakehouse后,同时Snowflake的上市,湖仓一体成为数据领域最火热的话题。


https://databricks.com/blog/2020/01/30/what-is-a-data-lakehouse.html
核心的理念是Delta Lake使Hadoop有了ACID事务能力,使用Spark内存做实时,批,AI计算层。
一体机 | Oracle Exadata,Teradata | 价格,软硬件无法解耦 |
MPP | Greenplum | 数据分片始终会遇到数据倾斜问题 |
数据库 | Oracle,SQL Server | 100T以下容量数仓,使用列存储索引 |

数据湖仓库可以分解为两种类型的架构:
第一种是数据湖(以使用读取存储模式的形式),称之为NOEDW,
第二种数据湖和关系数据库(以企业数据仓库或 EDW 的形式),将称之为ProEDW
对于 NoEDW,我的思维过程是,如果您试图使数据湖像关系数据库一样工作,为什么不只使用关系数据库呢?然后让数据湖做它擅长的事情, 关系数据库做它擅长的事情?
将关系数据库纳入数据湖仓的额外成本、复杂性和价值时间是值得的,原因有很多,其中之一是关系数据库将元数据与数据相结合,与在许多情况下元数据与数据分离的数据湖相比,自助 BI 更容易。当您处理来自许多不同来源的数据时,这种情况变得更加明显。
https://www.jamesserra.com/archive/2021/01/data-lakehouse-defined/
目前最成熟的商业产品就是Azure Synapse Analytics

功能有很多就不展开介绍了,这次只谈Azure Synapse Analytics Serverless,核心有2点:

https://docs.microsoft.com/zh-cn/azure/synapse-analytics/sql/on-demand-workspace-overview
另外支持Azure Cosmos DB做HTAP
https://docs.microsoft.com/zh-cn/azure/cosmos-db/synapse-link


Azure Data Lake在对象存储的基础上增加了文件层级目录结构,面向大数据分析优化。

https://docs.mihttps://docs.microsoft.com/zh-cn/azure/synapse-analytics/sql/query-data-storage



/*分页查询*/ select countries_and_territories, year, month, day, cases from openrowset( bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet', format = 'parquet') as rows order by year, month, day OFFSET 30 ROWS FETCH NEXT 10 ROWS ONLY; /*字符组合*/ select top 10 geo_id, year, month, cases = '{' + string_agg(concat('"',day,'":',cases), ',') within group (order by day asc) + '}' from openrowset( bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet', format = 'parquet') as rows group by geo_id, year, month; /*透视*/ with cases as ( select countries_and_territories, year, month, day, cases from openrowset( bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet', format = 'parquet') as rows ) select top 10 * from cases PIVOT ( SUM (cases) FOR day IN ( [1], [2], [3], [4], [5], [6], [7]) ) AS months
/*逆透视*/ with cases as ( select countries_and_territories, year, month, day, cases, deaths from openrowset( bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet', format = 'parquet') as rows where continent_exp = 'Europe' and year = 2020 ) select top 100 countries_and_territories, year, month, day, property, value from cases UNPIVOT ( value FOR property IN ( [cases], [deaths] ) ) AS unpivoted /*会话上下文*/ CREATE OR ALTER VIEW cases AS SELECT * FROM openrowset( bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet', format = 'parquet') as rows WHERE continent_exp = CAST(SESSION_CONTEXT(N'continent') AS VARCHAR(8000)) exec sp_set_session_context 'continent', 'Europe'; SELECT TOP 10 * FROM cases
不支持缓存,不支持查询Azure SQL/PostgreSQL/SQL DW
//估计今年会支持这2大特性
由于是按查询量计费,目前版本没有缓存,不适合高频查询。将数据全加载在Power BI内存模型,Power BI访问是按用户计费,可以无限查询访问,刚好解决此问题,定时刷新报告即可。还可利用Power BI的聚合表,复合模型特性功能。
聚合表

https://docs.microsoft.com/zh-cn/power-bi/transform-model/desktop-aggregations
复合模型

Power BI本身可以直接查询Azure Data Lake,不需要通过Azure Synapse Analytics Serverless,两者的比较可以查看相关文章。
https://blog.crossjoin.co.uk/2021/01/24/comparing-the-performance-of-importing->https://blog.crossjoin.co.uk/2021/01/31/comparing-the-performance-of-importing->https://datamonkeysite.com/2021/01/07/pushdown-filters-in-synapse-serverless-from-powerbi/
另外Power BI已支持Parquet文件。

SSAS模型中表的不同分区可以是不同数据源,做冷热分离。

有许多情况下,您可能需要从 Azure SQL 数据库访问放置在 Azure 数据湖上的外部数据,但Azure SQL目前不支持Polybase虚拟化查询外表(SQL Server可以)。可以通过Synapse Analytics Serverless间接实现。

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | /*Synapse中创建外表*/CREATE EXTERNAL TABLE csv.YellowTaxi ( pickup_datetime DATETIME2, dropoff_datetime DATETIME2, passenger_count INT, ...) WITH ( data_source= MyAdls, location = '/**/*.parquet', file_format = ParquetFormat);/*Azure SQL中建外部数据源*/CREATE MASTER KEY ENCRYPTION BY PASSWORD = 'put some strong password here';GOCREATE DATABASE SCOPED CREDENTIAL SynapseSqlCredential WITH IDENTITY = '<synapse sql username>', SECRET = '<synapse sql password>'; GOCREATE EXTERNAL DATA SOURCE SynapseSqlDataSourceWITH ( TYPE = RDBMS, LOCATION = '<synapse workspace>-ondemand.sql.azuresynapse.net', DATABASE_NAME = 'SampleDB', CREDENTIAL = SynapseSqlCredential);GO/*Azure SQL中建Synapse外表*/CREATE SCHEMA csv;GOCREATE EXTERNAL TABLE csv.YellowTaxi( vendor_id VARCHAR(100) COLLATE Latin1_General_BIN2, pickup_datetime DATETIME2, dropoff_datetime DATETIME2, passenger_count INT, trip_distance FLOAT, rate_code INT, store_and_fwd_flag VARCHAR(100) COLLATE Latin1_General_BIN2, pickup_location_id INT, dropoff_location_id INT, payment_type INT, fare_amount FLOAT, extra FLOAT, mta_tax FLOAT, tip_amount FLOAT, tolls_amount FLOAT, improvement_surcharge FLOAT, total_amount FLOAT)WITH ( DATA_SOURCE = SynapseSqlDataSource ); |
https://devblogs.microsoft.com/azure-sql/read-azure-storage-files-using-synapse-sql-external-tables/
MPP始终会遇到数据倾斜问题,当前大部分关系数据库已经支持列存储,是否需要MPP数仓值得考虑。使用大数据Kimball模式,数据湖+数据集市。
Synapse Analytics Serverless+Azure SQL(列存储表)就是此模式。SQL Server 2019的列存储表已经非常完美。

Azure Synapse Analytics默认开通服务器版本,不使用MPP SQL的情况下费用很低,推荐大家去体验下,下面连接是官方学习课程。
同时还有另外一种数据概念,数据网格,由Thoughtworks提出。可以查看这篇博客文章
https://www.jamesserra.com/archive/2021/02/data-mesh/

https://azure.microsoft.com/zh-cn/blog/4-common-analytics-scenarios-to-build-business-agility/

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