High-quality information is crucial in knowledge pipelines because its accuracy directly affects the validity of business insights generated from that data. As organizations operate, many utilize standardized protocols to codify and enact high-quality knowledge management guidelines across various settings, both in-office and remotely. Despite this, a pressing concern for organisations remains providing customers with transparency into the health and dependability of their intellectual assets. Within the context of enterprise knowledge catalogs, it’s crucial that customers rely on the trustworthiness of the information to make informed decisions? As information becomes outdated and refreshed, there is a risk of high-quality degradation due to upstream process fluctuations.
Amazon DataZone is a cutting-edge information management platform engineered to seamlessly facilitate knowledge discovery, cataloging, collaboration, and regulatory compliance. The text enables your group to centralize knowledge sharing by providing a secure, unified platform for accessing, collaborating, and discovering information across AWS, on-premises, and external sources. The platform streamlines information input for analysts, engineers, and corporate clients, enabling effortless discovery, utilization, and dissemination of expertise. Professional information producers, acting as knowledge custodians, can enhance the sharing of accurate and regulated data through pre-approved contextual additions and management entries. The following diagram provides a high-level overview of Amazon’s DataZone architecture. To gain a deeper understanding of Amazon DataZone’s fundamental features, refer to.
Amazon DataZone has streamlined its process by seamlessly integrating with AWS Glue’s Information Quality feature, allowing users to visualize data quality scores directly within the Amazon DataZone portal, thereby enhancing information reliability and accuracy. Insights are available regarding high-quality knowledge scores across various key performance indicators, including metrics for knowledge completeness, uniqueness, and accuracy.
By providing a comprehensive overview of the information’s high-quality validation guidelines applied to the data asset, you can make informed decisions regarding the suitability of the specific knowledge assets for their intended use? Amazon DataZone provides comprehensive visibility into the historical quality trends of an asset, enabling users to track changes in its performance over time. With Amazon DataZone APIs, knowledge owners can integrate high-quality guidelines from third-party sources into a specific knowledge asset. This screenshot showcases a concrete example of valuable data-driven insights seamlessly integrated into the Amazon DataZone business catalog. To study extra, see .
In this article, we propose approaches to capture information quality metrics for knowledge assets generated in .
Amazon Redshift is a fast, highly scalable, and fully managed cloud data warehouse that enables you to process and execute complex SQL analytics workloads on both structured and semi-structured data. Amazon DataZone natively facilitates knowledge sharing for Amazon Redshift data ownership.
With Amazon DataZone, data owners can seamlessly import technical metadata from a Redshift database, including tables and views, into their DataZone challenge’s repository. As these knowledge assets are imported into Amazon DataZone, they circumvent the traditional pathway through AWS Glue’s Information Catalog, thereby creating a gap in knowledge quality integration that may lead to inconsistencies and inaccuracies. The proposed solution aims to counterbalance Amazon Redshift’s knowledge repository by leveraging knowledge quality scores and key performance indicators (KPIs).
Resolution overview
A proposal has been made to design an ETL pipeline that utilizes various tools to extract, transform, and load data, with the goal of validating knowledge quality and delivering the information quality results to Amazon DataZone for further analysis. The accompanying screenshot visualizes this pipeline.
Upon initiating the pipeline, it establishes a secure connection to Amazon Redshift, subsequently applying high-quality guidelines as specified in AWS Glue, tailored to meet the organization’s distinct business requirements. Upon application of the governing criteria, the pipeline verifies data conformity with those standards. The outcome of these principles is subsequently deployed to Amazon DataZone through a tailored visual transformation that leverages Amazon DataZone’s application programming interfaces (APIs).
By integrating customized, visible remodeling within the knowledge pipeline, advanced Python code logic becomes easily reusable, enabling knowledge engineers to encapsulate and integrate it into their own pipelines, thereby ensuring high-quality information outputs. The remodel can be utilised independently of the supply knowledge being examined.
Each enterprise unit is empowered to maintain full autonomy in establishing and leveraging its own tailored knowledge quality standards, uniquely suited to its specific domain. To ensure the reliability and validity of their expertise. The pre-built, customized remodel serves as the cornerstone for each business entity, enabling them to efficiently integrate it into their respective domain-specific pipelines and streamline the process. To seamlessly integrate domain-specific knowledge and produce high-quality outcomes, each business unit can leverage a customized visual remodel by reusing code libraries and configuring parameters akin to those found within Amazon DataZone, where data quality results can be effortlessly posted with designated area, position, title, desk, and schema.
To seamlessly integrate AWS Glue’s data quality ratings and results with a Redshift table in Amazon DataZone, follow these straightforward steps.
Conditions
To achieve optimal observation, consider having the following:
AWS Glue Studio utilizes a tailored visual reworking approach to elevate data quality scores. Consult with these resources for further information.
By utilizing a tailored visual overhaul, teams are empowered to define, reapply, and disseminate enterprise-driven ETL strategies alongside their peers. Each enterprise unit can develop tailored quality control measures specific to their domain, leveraging a customized visualization framework to drive improved data quality outcomes and integrate these metrics seamlessly into Amazon DataZone alongside their existing knowledge assets. This approach eliminates potential inconsistencies arising from similar logic implementations across multiple codebases, facilitating a faster development cycle and enhanced efficiency.
To successfully execute a customized remodel using Amazon S3 and AWS Glue, ensure that you upload two specific files to the designated S3 bucket within the same AWS account where you plan to run the Glue job. Obtain the next recordsdata:
Transfer these data files to your AWS Glue assets in an Amazon S3 bucket, into the designated folder. transforms (s3://aws-glue-assets–/transforms). AWS Glue Studio will automatically discover and process all JSON records by default. transforms The folder resides in the same AWS S3 bucket.
Discover how to build a comprehensive ETL pipeline for data quality validation using AWS Glue Studio, as we walk you through each step in this comprehensive guide.
CREATE EXTERNAL TABLE etl_input_data (
id STRING,
name STRING,
age INT
)
PARTITIONED BY (date_key DATE)
LOCATION ‘s3://my-bucket/etl-input-data/’
TBLPROPERTIES (“skip.header.line.count”=”1”);
CREATE EXTERNAL TABLE transformed_data (
id STRING,
first_name STRING,
last_name STRING,
age INT,
date_key DATE
)
PARTITIONED BY (date_key DATE)
LOCATION ‘s3://my-bucket/transformed-data/’
TBLPROPERTIES (“skip.header.line.count”=”1”);
CREATE JOB IF NOT EXISTS my_etl_job;
CREATE VERSION AS 3;
BEGIN etl_job;
P = LOAD ‘etl_input_data’ USING org.apache.hadoop.mapred.lib.MULTI_FILE_INPUTFORMAT AS (id, name, age);
C = MAP EACH item AS (item)
SELECT id, SUBSTRING(name, 1, POSITION(‘ ‘ IN name)-1) as first_name,
SUBSTRING(name, POSITION(‘ ‘ IN name)+1) as last_name,
age, date_key;
D = STORE C INTO ‘transformed_data’;
END;
ALTER JOB my_etl_job SET DEFAULT_VERSION 3;
Why not consider leveraging AWS Glue for Spark to gain insights from and interact with tables in Redshift databases? AWS Glue provides native support for Amazon Redshift. Click on the AWS Glue console and select “Create a brand new visible ETL job”.
To set up an Amazon Redshift connection in your preferred data analysis tool or application, follow these steps:
1. First, you need to have an AWS account and then create a new Redshift cluster by going to the AWS Management Console and navigating to the Amazon Redshift dashboard.
2. Then, create a new database and specify the cluster name, node type, and number of nodes as per your requirements.
3. Next, go back to your data analysis tool or application and look for the option to add a new connection or data source.
4. Search for Amazon Redshift and select it from the list of available options. You may need to enter some basic information such as the cluster name, database name, username, and password.
5. Once you’ve entered all the necessary details, test the connection by clicking the “Test” button or running a simple query.
6. If everything is set up correctly, you should be able to view your Redshift data in your preferred application.
Within the job pane, select Amazon Redshift because the supply. For Redshift connectionSelect the established connection, subsequently designate the pertinent schema, and determine the workstation wherein the information high-quality assessments will be leveraged accordingly.
Applying rigorous standards to validate and verify the supply ensures compliance with industry best practices and regulatory requirements.
To proceed, simply click on the “Consider Information Quality” node and include it in your project’s visible job editor. This node enables you to structure and implement domain-specific expertise by utilizing high-quality guidelines tailored to your area of expertise. Once the principles have been established, you may choose to generate high-quality outputs. The outcomes of these guidelines will be stored within a designated Amazon S3 bucket, ensuring secure and centralized data management. You will be able to select from publishing high-quality results, setting alert notifications based on pre-defined thresholds, and more.
Preview knowledge high quality outcomes
Ensuring top-tier results, automated systems seamlessly generate the novel node. ruleOutcomes. Are you looking to achieve high-quality outcomes from the preview of information? ruleOutcomes The node is illustrated within this screenshot. The node produces information on high-quality outcomes, along with the results of each rule and its corresponding failure reason.
Amazon DataZone is a cloud-based data catalog and governance platform that enables organizations to discover, manage, and govern their data assets. To submit high-quality outcomes to Amazon DataZone, follow these steps:
? Identify key performance indicators (KPIs) for your organization’s data quality initiatives. This could include metrics such as accuracy, completeness, and timeliness of data.
? Establish clear goals and objectives for your data governance program. For example, you may aim to reduce data errors by 30% within the next quarter or increase data accessibility by 25%.
? Develop a comprehensive data catalog that includes metadata about your organization’s data assets. This should include information such as data provenance, lineage, and usage patterns.
? Implement a robust data quality control process that ensures high-quality outcomes. This could involve using data validation rules, data cleansing tools, and regular data audits to identify and correct errors.
? Provide training and support for users on how to properly use Amazon DataZone and ensure that they understand the importance of maintaining high-quality data outcomes.
? Continuously monitor and evaluate your data governance program to ensure it is meeting its goals and objectives. This should involve tracking key performance indicators, conducting regular assessments, and making adjustments as needed.
By following these steps, you can ensure that your organization’s data outcomes are high-quality, accurate, and reliable.
The output of the ruleOutcomes The node is subsequently forwarded to a bespoke visual reconfiguration module. After each dataset are uploaded, the AWS Glue Studio visual editor automatically lists the updated schema mentioned in post_dq_results_to_datazone.json (on this case, Datazone DQ End result SinkAmong various different transformations. AWS Glue Studio parses JSON definition files to display remodel metadata, including title, description, and a record of parameters. The metadata for an information asset within the Amazon DataZone defines its structural organization by listing key parameters such as the position to be imagined, area ID, desk title, and schema name.
Fill within the parameters:
- The glue_crawler_security_configuration parameter is optional and will remain blank; it’s intended to be used only when your AWS Glue job executes within a linked account.
- Your Amazon DataZone ID can be found in the Amazon DataZone portal by navigating to the Person Profile Title.
- The Redshift supply model is a highly complex and data-driven approach that necessitates a rigorous methodology to ensure accuracy and precision. The models employed in this analysis are not merely identical, but rather carefully crafted to mirror the intricate relationships between variables and assumptions embedded within the initial Redshift supply model.
Moreover, the reliance on robust statistical techniques and large datasets enables the generation of highly accurate predictions that can inform strategic decisions with confidence.
- The clear and concise ruleset title on Amazon DataZone is: “Title for Similar Desks”.
- Will the script return all the various Amazon DataZone assets when there are multiple matches for a similar table and schema title?
import boto3
from botocore.exceptions import NoCredentialsError
import os
job_particulars = {
‘Job_Type’: ‘Data_Science’,
‘Task_Description’: ‘To extract data from Amazon S3 and process it using Boto3 library.’,
‘Required_Model’: ‘boto3:latest’
}
--additional-python-modules
boto3>=1.34.105
Save and execute the task.
The article discusses the technical details of efficiently populating Amazon DataZone with high-value insights. Within the post_dq_results_to_datazone.py Script: We optimized the code to extract metadata from AWS Glue, refining our approach to deliver high-quality results by implementing strategies for identifying the correct DataZone asset based on table information. If you’re curious about evaluating the code within the script, you’ll be able to do so.
After a complete AWS Glue ETL job run, you can navigate to the Amazon DataZone console to verify that high-quality data is accurately displayed on the associated asset webpage?
Conclusion
On this post, we showcased how to leverage the capabilities of AWS Glue’s Data Quality and Amazon DataZone to implement comprehensive data quality monitoring for your Amazon Redshift data assets. By converging the capabilities of these two providers, you can offer customers unparalleled transparency into data quality and reliability, thereby building trust and empowering them to make informed decisions through self-directed research across your organization.
If you’re looking to elevate the information quality of your Amazon Redshift environment and inform data-driven decision-making, we recommend exploring the synergy between AWS Glue’s data quality capabilities and Amazon DataZone, as well as the new preview for OpenLineage-compatible data lineage visualization within Amazon DataZone. Please refer to the following resources for additional information and in-depth guidance.
Serves as a Principal Specialist in Options Architecture for Data and Analytics platforms. For two decades, he had toiled away in the realm of analytics, his dedication unwavering. However, a sudden and significant shift occurred when he relocated to Canada: he transformed into a devoted Hockey Dad, his passion for the sport now rivaling that of his analytical pursuits.
Serves as a senior analytics specialist and options architect at Amazon Web Services (AWS). With a specialty in crafting exceptional analytics solutions, she excels across various sectors. She specialises in designing cloud-based knowledge ecosystems that facilitate real-time data streaming, scalable knowledge processing, and robust governance frameworks, empowering organisations to make informed decisions at speed.
Serves as a Senior Technical Product Supervisor within Amazon DataZone at AWS. She prioritizes optimizing knowledge discovery and curation processes to support effective knowledge analytics. She is passionate about streamlining the AI/ML and analytics path for clients, empowering them to excel in their daily responsibilities. Exterior to her labor, she has a passion for nature and outdoor activities, such as hiking and exploring, while also appreciating the quiet solitude of reading and learning through self-study.
