We are doing the data transformations using Google Dataproc and all our data is residing in Dataproc Hive tables. How do i transfer/move this data to BigQuery.
Transfer to BigQuery from Hive seems to have a standard pattern:
dump your Hive into Avro files
Load those files in BigQuery
See an example here: Migrate hive table to Google BigQuery
As mentioned above, take care about the types compatibility between Hive/Avro/BigQuery.
And for the first time I guess it would not hurt to do some validations by comparing that the tables on both Hive and BigQuery have the same data: https://github.com/bolcom/hive_compared_bq
Related
I have linked Firebase events to BigQuery and my goal is to pull the events into S3 from BigQuery using AWS Glue.
When you link Firebase to BigQuery, it creates a default dataset and a date-partitioned table something like this:
analytics_456985675.events_20230101
analytics_456985675.events_20230102
I'm used to querying the events in BigQuery using
Select
...
from analytics_456985675.events_*
where date >= [date]
However, when configuring the Glue ETL job, it refuses to work with this format for a table analytics_456985675.events_* I get this error message:
it seems the Glue job will only work when I specify a single table.
How can I create a Glue ETL job that pulls data from BigQuery incrementally if I have to specify a single partition table?
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.
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.
I have a requirement to deploy a presto server which can help me query data stored in ADLS in Avro file formats.
I have gone through this tutorial and it seems that the Hive is used as a catalogue/connector in presto to query from ADLS. Can I bypass Hive and have any connector to extract data from ADLS?
Can I bypass Hive and have any connector to extract data from ADLS?
No.
Hive here plays two roles here:
storage for metadata. It contains information like:
schema and table name
columns
data format
data location
execution
it is capable to read data from (HDFS) distributed file systems (like HDFS, S3, ADLS)
it tells how execution can be distributed.
Guys a very basic question but not able to decipher ,Please help me out.
Q1: When we create bigquery table using below command , the data resides in same Cloud Storage?
bq load --source_format=CSV 'market.cust$20170101' \
gs://sp2040/raw/cards/cust/20170101/20170101_cust.csv
Q2: let's say my data director is gs://sp2040/raw/cards/cust/ for customer file Table structure defined is:
bq mk --time_partitioning_type=DAY market.cust \
custid:string,grp:integer,odate:string
Everyday I create new dir in the bucket such as 20170101,20170102..to load new dataset. So after the data loaded in this bucket Do I need to fire below queries.
D1:
bq load --source_format=CSV 'market.cust$20170101' \
gs://sp2040/raw/cards/cust/20170101/20170101_cust.csv
D2:
bq load --source_format=CSV 'market.cust$20170102' \
gs://sp2040/raw/cards/cust/20170102/20170102_cust.csv
When we create bigquery table using below command , the data resides in same Cloud Storage?
Nope! BigQuery is not using Cloud Storage for storing data (unless it is federated Table linked to Cloud Storage)
Check BigQuery Under the Hood with Tino Tereshko and Jordan Tigani - you will like it
Do I need to fire below queries
Yes. you need to load those files into BigQuery, so you can query the data
Yes you would need load the data into BigQuery using those commands.
However, there are a couple of alternatives
PubSub and Dataflow: You could configure PubSub to watch your cloud storage and create notification when files are added, described here. You could then have Dataflow job that imported the file into BigQuery. DataFlow Documentation
BigQuery external tables: BigQuery can query cvs files that are stored in Cloud Storage without importing the data, as described here. There is wildcard support for filenames so it could be configured once. Performance might not be as good as directly storing items in BigQuery