I want to use Delta Lake tables in my Hive Metastore on Azure Data Lake Gen2 as basis for my company's lakehouse.
Previously, I used "regular" hive catalog tables. I would load data from parquet into a spark dataframe, and create a temp table using df.CreateOrReplaceTempView("TableName"), so I could use Spark SQL or %%sql magic to do ETL. After doing this, I can use spark.sql or %%sql on the TableName. When I was done, I would write my tables to the hive metastore.
However, what If I don't want to perform this saveAsTable operation, and write to my Data Lake? What would be the best way to perform ETL with SQL?
I know I can persist Delta Tables in the Hive Metastore through a multitude of ways, for instance by creating a Managed catalog table through df.write.format("delta").saveAsTable("LakeHouseDB.TableName")
I also know that I can create a DeltaTable object through the DeltaTable(spark, table_path_data_lake), but then I can only use the Python API and not sql.
Does there exist some equivalent of CreateOrReplaceTempView(), or is there a better way to achieve ETL with SQL without 'writing' to the data lake first?
However, what If I don't want to perform this saveAsTable operation, and write to my Data Lake? What would be the best way to perform ETL with SQL?
Not possible with Delta Lake since it relies heavily on a transaction log (_delta_log) under the data directory of a delta table.
Related
I am designing a Data Lake in Azure Synapse and, in my model, there is a table that will store a small amount of data (like 5000 rows).
The single parquet file that stores this data will surely be smaller than the smallest recommended size for a parquet file (128 MB) and I know that Spark is not optimized to handle small files. This table will be linked to a delta table, and I will insert/update new data by using the MERGE command.
In this scenario, regarding performance, is it better to stick with a delta table, or should I create a SQL relational table in another DB and store this data there?
It depends on multiple factors like the types of query you will be running and how often you want to run merge command to upsert data to delta.
But even if you do perform analytical queries, looking at the size of data I would have gone with relational DB.
I want to create a hive table which will store data with orc format and snappy compression. Will power bi be able to read from that table? Also do you suggest any other format/compression for my table?
ORC is a special file format only going to work with hive and its highly optimized for HDFS read operations. And power BI can connect to hive using hive odbc data connection. So, i think if you have to use hive all the time, you can use this format to store the data. But if you want flexibility of both hive and impala and use cludera provided impala ODBC driver, you can think of using parquet.
Now, both orc and parquet has their own advantages and disadvantages. And main deciding factor can be tools that access the data, how nested data is, and how many columns are there .
If you have many columns with nested data and if you want to use both hive and impala to access data, go with parquet. And if you have few columns with flat data structure and huge amount of data, go with orc.
Are two hive tables (native, external) always required for querying a DynamoDB table from an AWS EMR?
I have created a native hive table (CTAS, create table as select) using an hive external table that was mapped to a DynamoDB table. My (read) query times against external tables are slow and it uses up the read throughput versus native table are fast and read throughput is not consumed.
My questions:
Is this a standard practice/best practice i.e., create an external table mapped to a dynamodb table and then create a CTAS and query against CTAS for all read query use cases?
Where or how GSI's on dynamodb come into picture on hive side of things? Toward this curiosity I have tried to map my external hive table column to dynamodb GSI and some what expectedly saw NULLs.
So, back to #2 question was wondering how are GSI's used with a native or external hive table?
Thanks,
Answer is no.
However, from my observation if a hive native table data is backed (CTAS) by hive external table that is referencing a DynamoDb table: Read data is not accounted if you are querying hive native table from EMR. If you to take into account the periodic update (refresh data) of hive native table.
I'm looking at storing data in S3 in ORC format for querying with Athena.
I want to partition the data like so ...
.../year=2019/month=7/
... and bucketing the data further by id (each id will have multiple records for each month, there are lots of id's)
I want to be able to insert new data into this structure daily... I understand that I can't use the INSERT INTO statement from Athena because bucketed tables are not supported.
What would be the best way to insert data daily into a table of this structure? Is it even possible to do with bucketed data?
Cheers
Presto allows inserts into existing partitions of bucketed partitioned tables since Presto 312. If Athena does not support this, you can very easily run a Presto cluster yourself, e.g. using Starburst Presto AWS integration (I can recommend this for other reasons too, as it can be way cheaper than using Athena if you run more than just few queries. Disclaimer: I'm from Starburst)
I am currently having Hadoop-2, PIG, HIVE and HBASE.
I have an inputdata. I have loaded that data in HDFS.
I want to create staging data in this environment.
My query is -
In which BigData component, I should create Staging Table(Pig/HIVE/HBASE) ; this will have data coming in based on a condition? Later, we might want to run MapReduce Jobs with complex logic on it.
Please assist
Hive: If you have OLAP kind of workload and dont need realtime read/write.
HBase: If you have OLTP kind of workload. You need to do realtime/streaming read/write. Some batch or OLAP processing can be done by using MapReduce. SQL-like querying is possible by using Apache Phoenix.
You can run MapReduce job on HIVE and HBase both.
Anywhere you want. Pig is not an option as it does not have a metastore. Hive if you want SQL Like queries. HBase based on your access patterns.
When you run a Hive query on top of data it is converted into MR.
When you create it in Hive use Hive Queries & not MR. If you are using MR then use Pig. You will not benefit creating a Hive table on top of data.