Blog Post
Data Cataloging(Metadata) on Cloud
Srinivasa Rao • Apr 12, 2020
Unified Metadata design on various clouds AWS, GCP and Azure
1 Introduction
This blog covers collecting, storing and searching and discovering various metadata across different data lake work streams on various clouds.
2 Types of metadata
Technical Metadata (System generated - auto ingested)
- Table names, Column names
- Table descriptions, column descriptions
- Data created; date modified
Business Metadata (User provided/inferred)
- PII information (Table and Column level)
- Table Owner
- Table Source
- Business logic to derive column
- Retain until date
- Data Quality Owner
- Data Governance
- Data Quality information
- Environment (prod/dev/qa/temp)
- Classify data
Data Pipeline Metadata (System generated, and User provided)
- Data pipelines
- ETL/ELT specific information
- Scheduling information
Data Lineage Metadata (automated through reusable components and system level defaults - for auditing only)
- Data last updated/created (add last updated and create timestamp to each row)
- Who updated the data (data pipeline, job name, user name and so on - Use Map or Struct or JSON column type)?
- How data was modified or added (storing update history where required - Use Map or Struct or JSON column type)
Any solution we use to tackle above metadata should cover the following:
➔A unified view into all the data no matter where it is stored
➔Integration with analytical tools
➔A way to automatically build all metadata and keep it in sync with our data as it evolves
➔Should have Data Governance in place through IAM controls
➔Should have a proper API to integrate it with other tools like data pipelines.
➔Should be able to search through easily
Data Catalog (GCP)
Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, manage, and understand all their data in Google Cloud. It offers a simple and easy-to-use search interface for data discovery, a flexible and powerful cataloging system for capturing both technical and business metadata, and a strong security and compliance foundation with Cloud Data Loss Prevention (DLP) and Cloud Identity and Access Management (IAM) integrations.
AWS Glue Data Catalog
AWS Glue data catalog collects metadata from different data sources like amazon RDS, S3, RedShift and Dynamo and allows users to search and discover data from AWS provided UI or through APIs.
Azure Data Catalog
Azure Data Catalog is a fully managed cloud service. It can collect metadata from different data sources and allows users to search and discover data.
Note: The above images are courteous to respective clouds and taken from their documentation.
"About Author"
The author has extensive experience in Big Data Technologies and worked in the IT industry for over 25 years at various capacities after completing his BS and MS in computer science and data science respectively. He is certified cloud architect and holds several certifications from Microsoft and Google. Please contact him at srao@unifieddatascience.com if any questions.
Database types Realtime DB The database should be able to scale and keep up with the huge amounts of data that are coming in from streaming services like Kafka, IoT and so on. The SLA for latencies should be in milliseconds to very low seconds. The users also should be able to query the real time data and get millisecond or sub-second response times. Data Warehouse (Analytics) A data warehouse is specially designed for data analytics, which involves reading large amounts of data to understand relationships and trends across the data. The data is generally stored in denormalized form using Star or Snowflake schema. Data warehouse is used in a little broader scope, I would say we are trying to address Data Marts here which is a subset of the data warehouse and addresses a particular segment rather than addressing the whole enterprise. In this use case, the users not only query the real time data but also do some analytics, machine learning and reporting. OLAP OLAP is a kind of data structure where the data is stored in multi-dimensional cubes. The values (or measures) are stored at the intersection of the coordinates of all the dimensions.
This blog puts together Infrastructure and platform architecture for modern data lake. The following are taken into consideration while designing the architecture: Should be portable to any cloud and on-prem with minimal changes. Most of the technologies and processing will happen on Kubernetes so that it can be run on any Kubernetes cluster on any cloud or on-prem. All the technologies and processes use auto scaling features so that it will allocate and use resources minimally possible at any given time without compromising the end results. It will take advantage of spot instances and cost-effective features and technologies wherever possible to minimize the cost. It will use open-source technologies to save licensing costs. It will auto provision most of the technologies like Argo workflows, Spark, Jupyterhub (Dev environment for ML) and so on, which will minimize the use of the provider specific managed services. This will not only save money but also can be portable to any cloud or multi-cloud including on-prem. Concept The entire Infrastructure and Platform for modern data lakes and data platform consists of 3 main Parts at very higher level: Code Repository Compute Object store The main concept behind this design is “Work anywhere at any scale” with low cost and more efficiently. This design should work on any cloud like AWS, Azure or GCP and on on-premises. The entire infrastructure is reproducible on any cloud or on-premises platform and make it work with some minimal modifications to code. Below is the design diagram on how different parts interact with each other. The only pre-requisite to implement this is Kubernetes cluster and Object store.
Spark-On-Kubernetes is growing in adoption across the ML Platform and Data engineering. The goal of this blog is to create a multi-tenant Jupyter notebook server with built-in interactive Spark sessions support with Spark executors distributed as Kubernetes pods. Problem Statement Some of the disadvantages of using Hadoop (Big Data) clusters like Cloudera and EMR: Requires designing and build clusters which takes a lot of time and effort. Maintenance and support. Shared environment. Expensive as there are a lot of overheads like master nodes and so on. Not very flexible as different teams need different libraries. Different cloud technologies and on-premises come with different sets of big data implementations. Cannot be used for a large pool of users. Proposed solution The proposed solution contains 2 parts, which will work together to provide a complete solution. This will be implemented on Kubernetes so that it can work on any cloud or on-premises in the same fashion. I. Multi-tenant Jupyterhub JupyterHub allows users to interact with a computing environment through a webpage. As most devices have access to a web browser, JupyterHub makes it easy to provide and standardize the computing environment of a group of people (e.g., for a class of data scientists or an analytics team). This project will help us to set up our own JupyterHub on a cloud and leverage the cloud's scalable nature to support large groups of users. Thanks to Kubernetes, we are not tied to a specific cloud provider. II. Spark on Kubernetes (SPOK) Users can spin their own spark resources by creating sparkSession. Users can request several executors, cores per executor, memory per executor and driver memory along with other options. The Spark environment will be ready within a few seconds. Dynamic allocation will be used if none of those options are chosen. All the computes will be terminated if they’re idle for 30 minutes (or can be set by the user). The code will be saved to persistent storage and available when the user logs-in next time. Data Flow Diagram
Various data lake design patterns on the cloud. Build scalable and highly performing data lake on the Microsoft (Azure) cloud.
Various data lake design patterns on the cloud. Build scalable and highly performing data lake on the Amazon (AWS) cloud.
Various data lake design patterns on the cloud. Build scalable and highly performing data lake on the google (GCP) cloud.
Different strategies to fully implement DR and BCP across the toolset and resources you are currently using and probably will use in near future on GCP.
Google Cloud Platform offers Stackdriver, a comprehensive set of services for collecting data on the state of applications and infrastructure. Specifically, it supports three ways of collecting and receiving information
Data Governance on cloud is a vast subject. It involves lot of things like security and IAM, Data cataloging, data discovery, data Lineage and auditing. Security Covers overall security and IAM, Encryption, Data Access controls and related stuff. Please visit my blog for detailed information and implementation on cloud. https://www.unifieddatascience.com/security-architecture-for-google-cloud-datalakes Data Cataloging and Metadata It revolves around various metadata including technical, business and data pipeline (ETL, dataflow) metadata. Please refer to my blog for detailed information and how to implement it on Cloud. https://www.unifieddatascience.com/data-cataloging-metadata-on-cloud Data Discovery It is part of the data cataloging which explained in the last section. Auditing It is important to audit is consuming and accessing the data stored in the data lakes, which is another critical part of the data governance. Data Lineage There is no tool that can capture data lineage at various levels. Some of the Data lineage can be tracked through data cataloging and other lineage information can be tracked through few dedicated columns within actual tables. Most of the Big Data databases support complex column type, it can be tracked easily without much complexity. The following are some examples of data lineage information that can be tracked through separate columns within each table wherever required. 1. Data last updated/created (add last updated and create timestamp to each row). 2. Who updated the data (data pipeline, job name, username and so on - Use Map or Struct or JSON column type)? 3. How data was modified or added (storing update history where required - Use Map or Struct or JSON column type). Data Quality and MDM Master data contains all of your business master data and can be stored in a separate dataset. This data will be shared among all other projects/datasets. This will help you to avoid duplicating master data thus reducing manageability. This will also provide a single source of truth so that different projects don't show different values for the same. As this data is very critical, we will follow type 2 slowly changing dimensional approach which will be explained my other blog in detail. https://www.unifieddatascience.com/data-modeling-techniques-for-modern-data-warehousing There are lot of MDM tools available to manage master data more appropriately but for moderate use cases, you can store this using database you are using. MDM also deals with central master data quality and how to maintain it during different life cycles of the master data. There are several data governance tools available in the market like Allation, Collibra, Informatica, Apache Atlas, Alteryx and so on. When it comes to Cloud, my experience is it’s better to use cloud native tools mentioned above should be suffice for data lakes on cloud/