I have one testdata.dmp available in AWS s3 bucket and want to load data into panda dataframe. Looking for some solution, I've boto3 installed.
Your Oracle dump file testdata.dmp has a proprietary binary format maintained by Oracle. This means that Oracle controls which tools can process it correctly. One of such tools is Oracle Data Pump.
A workflow to extract data from a Oracle dump file and write it as Parquet files (readable with Pandas) could look as follows:
Create an Oracle DB. As you are already using AWS S3, I suggest setting up an AWS RDS instance with Oracle engine.
Download testdata.dmp from S3 to the created Oracle DB. This can be done by RDS' S3 integration.
Run Oracle Data Pump Import on the RDS instance. This tool is installed by default. The RDS docs provide a detailed walk-through. Now the content of testdata.dmp lives as tables with data and other objects inside the Oracle DB.
Dump all tables (and other objects) with a tool that is able to query Oracle DBs and able to write the result as Parquet. Some choices:
Sqoop (Hadoop-based command line tool, but deprecated)
(Py)Spark (Popular data processing tool and imho the unofficial successor of Sqoop.)
python-oracledb + Pandas
Related
Using Python-3, I am trying to compare an Excel (xlsx) sheet to an identical spark table in Databricks. I want to avoid doing the compare in Databricks. So I am looking for a way to read the spark table via the Databricks api. Is this possible? How can I go on to read a table: DB.TableName?
There is no way to read the table from the DB API as far as I am aware unless you run it as a job as LaTreb already mentioned. However, if you really wanted to, you could use either the ODBC or JDBC drivers to get the data through your databricks cluster.
Information on how to set this up can be found here.
Once you have the DSN set up you can use pyodbc to connect to databricks and run a query. At this time the ODBC driver will only allow you to run Spark-SQL commands.
All that being said, it will probably still be easier to just load the data into Databricks, unless you have some sort of security concern.
I can recomend you write pyspark code in notebook, call the notebook from previously defined job, and establish connection between your local machine and databricks workspace.
You could perfom comaprision directly on spark or convert data frames to pandas if you wish. If noteebok will end comaprision, could retrun result from particular job. I think that sending all databricks tables could be impossible because of API limitation you have spark cluster to perform complex operation, API should be use to send small messages.
Officical documentation:
https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/jobs#--runs-get-output
Retrieve the output and metadata of a run. When a notebook task
returns a value through the dbutils.notebook.exit() call, you can use
this endpoint to retrieve that value. Azure Databricks restricts this
API to return the first 5 MB of the output. For returning a larger
result, you can store job results in a cloud storage service.
Does Snowflake support JDBC data sources, and if so how? I'm using Netsuite Analytics as a datasource and would like to load that to a Snowflake warehouse. The examples I'm finding for SnowFlake are file readers, I realise I can convert my netsuite data to a file and then ingest that but I'd rather remove that additonal step.
Snowflake has both ODBC and JDBC drivers that you can use. However, if you are loading a lot of data from Netsuite Analytics, most of the Snowflake drivers will actually generate files, PUT them to S3, and execute a COPY INTO statement to get the data into Snowflake for you. While it is more seamless, it is still executing that "additional step". The reason is...that's the most efficient way to get data into Snowflake, and it's not even close.
https://docs.snowflake.com/en/user-guide/odbc.html
https://docs.snowflake.com/en/user-guide/jdbc.html
No, Snowflake doesn't offer tools for loading data from JDBC or ODBC data sources. This is because Snowflake is a database platform and the functionality you're describing is that of a data integration or ETL tool. There are plenty of third party tools available that can handle this such as Matillion or Talend. Snowflake has a list of recommended technology partners on their website.
If you don't have access to an ETL tool then, as you mentioned, you can create a process yourself to export data from Netsuite to files that are uploaded to cloud storage such AWS S3. You can then set up this storage area an "external stage" and use Snowflake's COPY statement to load the data into Snowflake.
Do you need to ingest excel and other proprietary formats using glue or allow glue to work crawl your s3 bucket to use these data formats within your data lake?
I have gone through the "Data Lake Foundation on the AWS Cloud" document and am left scratching my head about getting data into the lake. I have a Data Provider with a large set of data stored on their system as excel and access files.
Based on the process flow they would upload the data into the submission s3 bucket, which would set off a series of actions, but there is no etl of the data into a format that would work with the other tools.
Would using these files require using glue on the data that is submitted in the bucket or is there another way to make this data available to other tools such as Athena and redshift spectrum?
Thank you for any light you can shed on this topic.
-Guido
I'm not seeing that can take excel data directly to Data Lake. You might need to convert into CSV/TSV/Json or other formats before loading into Data Lake.
Formats Supported by Redshift Spectrum:
http://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-data-files.html -- Again I don't see Excel as of now.
Athena Supported File Formats:
http://docs.aws.amazon.com/athena/latest/ug/supported-formats.html -- I don't see Excel also not supported here.
You need to upload the files to S3 either to Use Athena or Redshift Spectrum or even Redshift storage itself.
Uploading Files to S3:
If you have bigger files, you need to use S3 multipart upload to upload quicker. If you want more speed, you need to use S3 accelerator to upload your files.
Querying Big Data with Athena:
You can create external tables with Athena from S3 locations. Once you create external tables, use Athena Sql reference to query your data.
http://docs.aws.amazon.com/athena/latest/ug/language-reference.html
Querying Big Data with Redshift Spectrum:
Similar to Athena, you can create external tables with Redshift. Start querying those tables and get the results on Redshift.
Redshift has lot of commercial tools, I use SQL Workbench. It is free open source and rock solid, supported by AWS.
SQL WorkBench: http://www.sql-workbench.net/
Connecting your WorkBench to Redshift: http://docs.aws.amazon.com/redshift/latest/mgmt/connecting-using-workbench.html
Copying data to Redshift:
Also if you want to take the data storage to Redshift, you can use the copy command to pull the data from S3 and its gets loaded to Redshift.
Copy Command Examples:
http://docs.aws.amazon.com/redshift/latest/dg/r_COPY_command_examples.html
Redshift Cluster Size and Number of Nodes:
Before creating Redshift Cluster, check for required size and number of nodes needed. More number of nodes gets query parallely running. One more important factor is how well your data is distributed. (Distribution key and Sort keys)
I have a very good experience with Redshift, getting up to the speed might take sometime.
Hope it helps.
I am trying to migrate an entire table from my RDS instance (MySQL 5.7) to either S3 (csv file) or Hive.
The table has a total of 2TB of data. And it has a BLOB column which stores a zip file (usually 100KB, but it can reach 5MB).
I made some tests with Spark, Sqoop and AWS DMS, but had problems with all of them. I have no experience exporting data from RDS with those tools, so I really appreciate any help.
Which one is the most recommended for this task? And what strategy do you think is more efficient?
You can copy the RDS data to S3 using AWS pipeline. Here is an example which does the very thing.
Once you taken the dump to S3 in csv format it is easy to read the data using spark and register that as Hive Table.
val df = spark.read.csv("s3://...")
df.saveAsTable("mytable") // saves as hive
Is it possible to use Amazon Redshift as the data source for an Excel pivot table? Googling this question didn't yield any obvious answers. Thanks.
Yes I have.
However since the other answers were written, rather than use generic PostGres drivers, you should use customised Redshift Drivers provided by Amazon.
The answers you are looking for are here:
http://docs.aws.amazon.com/redshift/latest/mgmt/configure-odbc-connection.html
You can consume Amazon Redshift databases with the PostGRESQL ODBC drivers.
Download and install driver.
Set up a DSN on the box pointed to your Redshift server with your AWS credentials (you can find the ODBC connection string in the settings area of your cluster.)
Use that connection in Excel or any other product that can connect to ODBC connections.
You can convert Excel to CSV and upload it to S3. Once files are uploaded to S3 you can run copy command to copy data from S3 to Redshift cluster. You can run copy command via PostGRESQL JDBC connector or available tools like SqlWorkbench.