I created a cluster and connected to the database via SQL Workbench, but how can I upload data via SQL to Amazon Redshift?
I guess I have to use Amazon S3 but I could not find a sample video or text that describes it well.
There are two ways to insert information into Amazon Redshift:
Via the COPY command
Via INSERT statements
It is not recommended to use INSERT statements because they are not efficient for large data volumes. They are okay for doing ETL-type processes such as copying data between tables, but as a general rule data should be loaded via COPY.
As per Using a COPY Command to Load Data, the COPY command can load data from:
Amazon S3 (recommended, highly parallel)
Amazon EMR (Hadoop)
Amazon DynamoDB
Via SSH from remote hosts
The load from Amazon S3 is performed in parallel across all nodes and is the most efficient way to load data.
The Amazon Redshift COPY command can read several file formats:
Delimited (eg CSV)
Fixed-Width
AVRO
JSON
And these formats can also be compressed (eg gzip)
Bottom line: Get your data into Amazon S3 in a compatible format, then use COPY to load it.
Also, try to understand DISTKEY and SORTKEY to get full performance benefits out of Redshift. Definitely read the manual -- it will save you more time than it takes to read!
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I have multiple data source from which I need to build and implement a DWH in AWS. I have one challenge with respect to one of my unstructured data source (Data coming from different APIs). How can I ingest data from this source into the Amazon Redshift??? Can we first pull it into Amazon S3 bucket and then integrate S3 with Amazon redshift? What is a better approach?
Yes, S3 first. You APIs can write to S3 or/and if you like you can use a service like Kinesis (with or without firehose) to populate S3. From there it is just work in Redshift.
Without knowing more about the sources, yes S3 is likely the right approach - whether you require latency in seconds, minutes or hours will be an important consideration.
If latency is not a driving concern, simply:
Set up an S3 bucket to use a destination from your initial source(s).
Create tables in your Redshift database (loading data from S3 to Redshift requires pre-existing destination table).
Use the COPY command load from S3 to Redshift.
As noted, there may be value in Kinesis, especially if you're working with real-time data streams (the service recently introduced support for skipping S3 and streaming directly to Redshift).
S3 is probably the easier approach, if you're not trying to analyze real-time streams.
Is it possible to upload data directly to Amazon Redshift without passing through Amazon S3 (Using Talend)?
It is possible to do this using talend connectors for postgres, but the result would be very slow indeed (could be seconds per row of data).
You really need to
split large csv files up e.g. 10MB each (no set number for this)
gzip each csv file
upload to s3
run a redshift copy command
run some sql on redshift if required to process the new data (upsert
for example)
It is possible using INSERT queries, but is not at all efficient, and very slow, and thus, not recommended. Redshift is built for handling and managing bulk loads.
Using COPY command to load data into Redshift after splitting the large files into smaller parts, using multi-part file upload to S3 and then loading the data from S3 to Redshift using COPY command, in parallel (see this), is the best and fastest approach to load data into Redshift.
We have large amount of data stored on ES cluster. I need to add one more field to the ES cluster and upload data for this field from Redshift table’s column. I’ve never work with such data transfer, and I’m new to AWS and not sure how to approach this task and what I should read to perform such data transfer. Do you know what is the best approach to do it?
Are you using logstash doing just the data if yes then you can easily add column in logstash. And restart the lock start from the beginning so that the additional column data is ingested into the index. Let me know what is your current setup.
As i understand you want to dump data from elasticssearch cluster and load it to redshift.
Here is a approach i would take:
Dump data from elasticsearch using:https://github.com/taskrabbit/elasticsearch-dump
Copy the json file to s3 : using aws cli
Copy the json file from s3 to redshift using : https://docs.aws.amazon.com/redshift/latest/dg/copy-usage_notes-copy-from-json.html
Can anyone explain me clearly about what kind of data Redshift can handle(like structured, unstructured , or in any formats)?
How to copy Cloudfront logs into Amazon Redshift even the log is in unstructured data without going to Amazon EMR?
**How to find Database size which is created in Amazon Redshift?
Please someone explain me clearly about all the three questions which i have mentioned it above...It will be better if you explain me with some example or sample code or any source it will be very helpful for my project
Amazon Redshift provides a standard SQL interface (based on PostgreSQL). Therefore, it is best suited for structured data that is stored in Tables, Rows and Columns.
It is also possible to store JSON records within a field and access them via JSON functions.
To load data into Amazon Redshift, it needs to be in a delimited file format, such as comma delimited, tab delimited, fixed-length fields or JSON format. Any data that is not in a suitable format will need to be pre-processed and converted to a suitable format. This could be done with tools such as Amazon Athena (Presto) or Amazon EMR (Hadoop).
Amazon CloudFront logs are in Tab-Delimited format and can be loaded directly into Amazon Redshift. For an example, see: Analyzing S3 and CloudFront Access Logs with AWS Redshift
Information about disk space consumed by tables can be obtained via the SVV_DISKUSAGE system view.
I have a local Hadoop cluster and want to load data into Amazon Redshift. Informatica/Talend is not an option considering the costs so can we leverage Sqoop to export the tables from Hive into Redshift directly? Does Sqoop connect to Redshift?
The most efficient way to load data into Amazon Redshift is by placing data into Amazon S3 and then issuing the COPY command in Redshift. This performs a parallel data load across all Redshift nodes.
While Sqoop might be able to insert data into Redshift by using traditional INSERT SQL commands, it is not a good way to insert data into Redshift.
The preferred method would be:
Export the data into Amazon S3 as CSV format (preferably in .gz or .bzip format)
Trigger a COPY command in Redshift
You should be able to export data to S3 by copying data to a Hive External Table in CSV format.
Alternatively, Redshift can load data from HDFS. It needs some additional setup to grant Redshift acces to the EMR cluster. See Redshift documentation: Loading Data from Amazon EMR
copy command not supporting upsert it just simply load as many times as you mention and end up with duplicate data, so better way is use glue job and modify it for update else insert or use lambda to upsert into redshift