I have hundreds of Hive Queries(HQLs, using Hive functions like date_sub, lead, lag etc) which I need to convert to Redshift Spectrum, is there any tool which helps in this?
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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 have a task to extract data from BigQuery to PostgreSQL and I would like to know if it is possible to do this extraction using Google Dataform. I know the reverse (Postgres to BigQuery) is possible, but what about BigQuery to Postgres?
Thanks
Is there a way for us to check how frequently a table has been accessed/queried in AWS redshift?
Frequency can be daily/monthly/every hour or whatever.. Can some one help me?
It could be sql queries using system tables from AWS Redshift or some python script. What is the best way?
I want to insert Json to hive database.
I try to transform Json to SQL using ConvertJsonToSQL Ni-Fi processor. How can I use PARTITION (....) part into my query??
Can I do this or I should use ReplaceText processor for making query?
What version of Hive are you using? There are Hive 1.2 and Hive 3 versions of PutHiveStreaming and PutHive3Streaming (respectively) that let you put the data directly into Hive without having to issue HiveQL statements. For external Hive tables in ORC format, there are also ConvertAvroToORC (for Hive 1.2) and PutORC (for Hive 3) processors.
Assuming those don't work for your use case, you may also consider ConvertRecord with a FreeFormTextRecordSetWriter that generates the HiveQL with the PARTITION statement and such. It gives a lot more flexibility than trying to patch a SQL statement to turn it into HiveQL for a partitioned table.
EDIT: I forgot to mention that the Hive 3 NAR/components are not included with the NiFi release due to space reasons. You can find the Hive 3 NAR for NiFi 1.11.4 here.
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.