![]() ![]() The diagram illustrates high level flow and components of the solution approach. These documents could be from any of the industry domains. Now, let’s take a look at our solution approach for analyzing heavily nested json documents. The majority of existing analytics infrastructure relies on the “flat” storage and presentation of data assets this can be challenging given the schema-less JSON structure. AWS announced PartiQL, an open-source SQL-compatible query language, that makes it easy to efficiently query data, regardless of where or in what format it is stored. We focus on an approach that can help data analysts reduce the manual work and long cycles of nested json data processing by querying and running analysis that is required day to day. In this demo we will walk through the process of using Amazon Redshift PartiQL and the role it plays in simplifying the analysis of data in its native JSON format. ![]() This is a very large obstacle to the agility and flexibility needed to effectively use data lakes. Hence, if we want to change your data to another format or change the database engine we use to access/process that data (which is not uncommon in a data lake world), or change the location of the data, we may also need to change the application and queries. The result is tight coupling between the query language and the format in which data is stored. Every different type and flavor of data store may suit a particular use case, but each also comes with its own query language. Data may also reside in the data lake, stored in formats that may lack schema, or may involve nesting or multiple values (e.g., Parquet, JSON). Some data may be highly structured and stored in SQL databases or data warehouses, other data may be stored in NoSQL engines, including key-value stores, graph databases, ledger databases, or time-series databases. Data is typically spread across a combination of relational databases, non-relational data stores, and data lakes. But before we move on to that, let us first define the problem statement. As an example, try counting the contacts SELECT count(*) FROM spectrum_schema.In this blog, we will focus on understanding the process of using AWS Redshift PartiQL and how it can be used to analyze data in its native format. ![]() Also, in order to move the data from the s3 bucket to the newly created AWS Glue database, we will use a AWS Glue crawler.įirst, create a role that can be assumed by AWS Glue glue-role.json :role/dd-redshift' create external database if not exists We will define a AWS Glue database that can be queried from AWS Redshift. Notice that in order for the transfer to work, the service_account in question must have access to Dreamdata's bucket ( how to) and be attached to a Google Cloud Platform project with Cloud Billing enabledĪWS Glue will act as a layer in between your AWS s3 bucket, currently hosting the data, and your AWS Redshift cluster. ![]() With all pre-requisites done, you should be able to fill the following variables (which will be used throughout the integration) aws_account_id= redshift_cluster_id= service_account= gcs_name= Give your Google Cloud Storage service account permissions to access Dreamdata's Google Cloud Storage bucket.Dreamdata Google Cloud Storage destination enabled.GCP Service Account in a project with Cloud Billing enabled.AWS Glue is a service that can act as a middle layer between an AWS s3 bucket and your AWS Redshift cluster. Once connected, you can run your own queries on our data models, as well as copy, manipulate, join and use the data within other tools connected to Redshift. This document describes how to integrate your Dreamdata data with your AWS Redshift cluster. ![]()
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