How do I load data into Cassandra from Netezza? In doing that I also need to transform some tables. I have no experience in ETL. I would like to know how to start on this.
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I have tried connecting spark with JDBC connections to fetch data from MySQL / Teradata or similar RDBMS and was able analyse the data.
Can spark be used to store the data to HDFS?
Is there any possibility for spark outperforming
the activities of Sqoop.
Looking for you valuable answers and explanations.
There are two main things about Sqoop and Spark. The main difference is Sqoop will read the data from your RDMS doesn't matter what you have and you don't need to worry much about how you table is configured.
With Spark using JDBC connection is a little bit different how you need to load the data. If your database doesn't have any column like numeric ID or timestamp Spark will load ALL the data in one single partition. And then will try to process and save. If you have one column to use as partition than Spark sometimes can be even faster than Sqoop.
I would recommend you to take a look in this doc.enter link description here
The conclusion is, if you are going to do a simple export and that need to be done daily with no transformation I would recommend Sqoop to be simple to use and will not impact your database that much. Using Spark will work well IF your table is ready for that, besides that goes with Sqoop
I am trying to understand which of the below two would be better option especially in case of Spark environment :
Loading the parquet file directly into a dataframe and access the data (1TB of data table)
Using any database to store and access the data.
I am working on data pipeline design and trying to understand which of the above two options will result in more optimized solution.
Loading the parquet file directly into a dataframe and access the data is more scalable comparing to reading RDBMS like Oracle through JDBC connector. I handle the data more the 10TB but I prefer ORC format for better performance. I suggest you have to directly read data from files the reason for that is data locality - if your run your Spark executors on the same hosts, where HDFS data nodes located and can effectively read data into memory without network overhead. See https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-data-locality.html and How does Apache Spark know about HDFS data nodes? for more details.
I use Spark 2.0.2.
While learning the concept of writing a dataset to a Hive table, I understood that we do it in two ways:
using sparkSession.sql("your sql query")
dataframe.write.mode(SaveMode."type of
mode").insertInto("tableName")
Could anyone tell me what is the preferred way of loading a Hive table using Spark ?
In general I prefer 2. First because for multiple rows you cannot build such a long sql and second because it reduces the chance of errors or other issues like SQL injection attacks.
In the same way that for JDBC I use PreparedStatements as much as possible.
Think in this fashion, we need to achieve updates on daily basis on hive.
This can be achieved in two ways
Process all the data of the hive
Process only effected partitions.
For the first option sql works like a gem, but keep in mind that the data should be less to process entire data.
Second option works well.If you want to process only effected partition. Use data.overwite.partitionby.path
You should write the logic in such a way that it process only effected partitions. This logic will be applied to tables where data is in millions T billions records
I have deployed a 9 node DataStax Cluster in Google Cloud. I am new to Cassandra and not sure how generally people push the data to Cassandra.
My requirement is read the data from flatfiles and RDBMs table and load into Cassandra which is deployed in Google Cloud.
These are the options I see.
1. Use Spark and Kafka
2. SStables
3. Copy Command
4. Java Batch
5. Data Flow ( Google product )
Is there any other options and which one is best.
Thanks,
For flat files you have 2 most effective options:
Use Spark - it will load data in parallel, but requires some coding.
Use DSBulk for batch loading of data from command line. It supports loading from CSV and JSON, and very effective. DataStax's Academy blog just started a series of the blog posts on DSBulk, and first post will provide you enough information to start with it. Also, if you have big files, consider to split them into smaller ones, as it will allow DSBulk to perform parallel load using all available threads.
For loading data from RDBMS, it depends on what you want to do - load data once, or need to update data as they change in the DB. For first option you can use Spark with JDBC source (but it has some limitations too), and then saving data into DSE. For 2nd, you may need to use something like Debezium, that supports streaming of change data from some databases into Kafka. And then from Kafka you can use DataStax Kafka Connector for submitting data into DSE.
CQLSH's COPY command isn't as effective/flexible as DSBulk, so I won't recommend to use it.
And never use CQL Batch for data loading, until you know how it works - it's very different from RDBMS world, and if it's used incorrectly it will really make loading less effective than executing separate statements asynchronously. (DSBulk uses batches under the hood, but it's different story).
I have a spark job that right now pulls data from HDFS and transforms the data into flat files to load into the Cassandra.
The cassandra table is essentially 3 columns but the last two are map collections, so a "complex" data structure.
Right now I use the COPY command and get about 3k rows/sec load but thats extremely slow given that I need to load about 50milllion records.
I see I can convert the CSV file to sstables but I don't see an example involving map collections and/or lists.
Can I use the spark connector to cassandra to load data with map collections and lists and get better performance than just the COPY command?
Yes the Spark Cassandra Connector can be much much faster for files already in HDFS. Using spark you'll be able to distributedly grab and write into C*.
Even without Spark using a java based loader like https://github.com/brianmhess/cassandra-loader will give you a significant speed improvement.