Key Points

1. Data pipelines connect, transform data sources to data targets in batches or event streams
2. Semantic data services - data that can interpret, map other data sources semantically to a data context

References

Key Concepts

Supply Chain Data Examples Categorized by Structure, Volume Dimensions

Linkedin - Raj Grover - Transform Partners - Supply Chain Data Governance tip

Data Management Standards

1. Data Modeling Standards
2. Enterprise Metadata Standards
3. Data Architecture Standards
4. Data Quality Standards
5. Master Data Management Standards
6. Data Governance Standards & Polcies
7. Data Services Standards
8. Data Transformation Standards
9. Data Security Standards & Policies
10. Data Transaction Standards
11. Distributed Data Standards
12. Data Control Standards
13. Data Event Services Standards

Data Table Types

• Master Code Tables
• Meta model definition Tables
• Service Control Tables
• Process Control Tables
• Interface Control Tables
• Product Control Tables
• Environment Control Tables
• Event Control Tables
• Data Management Control Tables
• Configuration Tables
• Master Tables
• Transaction Tables 
• Transaction Ledgers
• Transaction Logs
• Event Transaction Tables
• Work Tables ( global, process, user )
• Work Management Tables 

Data Attribute Types

Character

Number

Decimal

Date

Boolean

Binary

OLTP 

Observability tools - common features for OLTP

oltp-observability-astronomer.io-Watch Demos On-Demand - Astronomer.pdf link

oltp-observability-astronomer.io-Watch Demos On-Demand - Astronomer.pdf file

OLAP

real-time on-demand access

smart caching and data age

data streaming

event streaming and reactive services

Enterprise Data Landscape Components

Raj Grover

https://www.linkedin.com/posts/rajkgrover_data-dataintegration-datagovernance-activity-7123621892367736832-NbI3?utm_source=share&utm_medium=member_desktop

Components of #Data Landscape:

 

The term "data landscape" refers to the comprehensive view of all data assets and their interactions within an organization. It encompasses the various data sources, data repositories, data flows, data processing, and data usages across the enterprise.



The data landscape provides a holistic perspective on how data is collected, stored, processed, and utilized to support business operations, decision-making, and strategic objectives.

 

Key components and aspects of a data landscape typically include:

 

-Data Sources: These are the origins of data, such as databases, applications, sensors, external partners, or any system that generates or captures data.


 

-Data Types: Different categories of data, including structured data (e.g., databases, spreadsheets) and unstructured data (e.g., text documents, images, videos).

 

-Data Repositories: The places where data is stored, including on premises databases, data warehouses, data lakes, cloud storage, and file systems.


 

-Data Flows: The pathways through which data moves within the organization. This includes data ingestion, transformation, movement between systems, and data export.


 

-Data Processing: The actions and transformations applied to data as it moves through the data landscape. This can involve data cleansing, aggregation, analytics, and reporting.


 

- #DataIntegration: The process of combining data from different sources to provide a unified view. Integration helps eliminate data silos and ensures data consistency.


 

-Data Usage: How data is utilized within the organization, including reporting, business intelligence, analytics, and other applications. This also covers data access and usage permissions.


 

- #DataGovernance: The policies, procedures, and controls governing data quality, security, privacy, and compliance throughout the data landscape.


 

- #Metadata: Information about the data, such as data definitions, data lineage, data ownership, and data quality indicators. Metadata is critical for understanding and managing the data landscape.


 

- #DataArchitecture: The structural design and organization of data assets, including data models, schemas, and data structures.

 

- #DataManagement Tools: The software and platforms used to manage and maintain the data landscape, such as data integration tools, data modeling tools, data cataloging tools, and data governance platforms.


 

- #DataSecurity and Access Control: Measures in place to protect sensitive data, including authentication, authorization, encryption, and data masking.


 

-Data Ownership and Stewardship: Roles and responsibilities for managing and maintaining data assets, including data owners and data stewards.


 

- #DataQuality and Data Profiling: Processes for assessing and improving the quality of data, as well as understanding the characteristics of data elements

Datacamp Data and Machine Learning Tools Landscape Resource Map

Identifies many data technologies, tools and resources that can be used to build, operate and support an Enterprise Data Landscape

 

Image Source: Datacamp

https://www.datacamp.com/blog/infographic-data-and-machine-learning-tools-landscape

Data Lake versus Data Warehouse

https://www.linkedin.com/posts/transformpartner_datalake-metadata-dataanalytics-activity-7093934239548633088-k2Eo?utm_source=share&utm_medium=member_desktop

When to use a data lake

Typical uses for a data lake include data exploration, data analytics, and #machinelearning.
A data lake can also act as the data source for a #datawarehouse. With this approach, the raw data is ingested into the data lake and then transformed into a structured queryable format. Typically this transformation uses an ELT (extract-load-transform) pipeline, where the data is ingested and transformed in place. Source data that is already relational may go directly into the data warehouse, using an ETL process, skipping the data lake.
Data lake stores are often used in event streaming or IoT scenarios, because they can persist large amounts of relational and nonrelational data without transformation or schema definition. They are built to handle high volumes of small writes at low latency, and are optimized for massive throughput.

m Apache Data Services

Data Exchange Stds - ISO20022

https://www.iso20022.org/sites/default/files/documents/general/Scripted_ISO_20022_ppt_long_version_v176.ppt

ISO_20022-fin-data-exchange-stds_ppt_long_version_v176.pdf

Data Exchange Formats - ISO ASN.1 std

ASN.1 Reference card

Serializable data formats with different encoding stds

concept of a schema and data

supports XML, JSON, BER, PER formats

ISO ASN.1 std reference card - gdrive

Contact ::= SEQUENCE {
name VisibleString,
phone NumericString
}

So, the above contact named John Smith with a phone number of 987 6543210 might be serialized in binary or text format (ASN.1 Encoding Rules). For example:

                     Basic Encoding Rules (BER)
       30 19  80 0A 4A6F686E20536D697468  81 0B 3938372036353433323130
 
                     Packed Encoding Rules (PER) 
       0A 4A 6F 68 6E 20 53 6D 69 74 68 0B A9 80 76 54 32 10
 
                     XML Encoding Rules (XER)    
       <?xml version="1.0" encoding="UTF-8"?> <Contact> 
       <name>John Smith</name> <phone>987 6543210</phone> </Contact> 
 
                     JSON Encoding Rules (JER)  
       { "name" : "John Smith", "phone" : "987 6543210" }

Data Quality - SOC 2 Data Quality Standards for Data Services

https://www.imperva.com/learn/data-security/soc-2-compliance/

SOC 2 is an auditing procedure that ensures your service providers securely manage your data to protect the interests of your organization and the privacy of its clients. For security-conscious businesses, SOC 2 compliance is a minimal requirement when considering a SaaS provider.

Developed by the American Institute of CPAs (AICPA), SOC 2 defines criteria for managing customer data based on five “trust service principles”—security, availability, processing integrity, confidentiality and privacy.

SQL Basics

https://datamovesme.com/2019/12/30/free-sql-for-data-science-course/

DDL

DML

see Jim Mason SQL Sessions

m IBM i and Z Solutions & Services#JimMason-BestSQLsession

NoSQL compared to SQL

https://www.w3resource.com/mongodb/nosql.php

https://drive.google.com/open?id=1UAwrKXa9PN3SG4V6IrVfLzdb3L_lgwnw

Transcript

Relations						Basic Relations
Data Manipulation Language(DML) No predefined schema
Indexes							Indexes
Structured query language(SQL)	Limited query capabilities
Transactions						Generally No
Structured and organized data	Unstructured and unpredictable data
Only Values						Values & references
Strongely typed					Loosely typed
Joins							No Joins

Redis DB Cache Management

https://www.w3resource.com/slides/redis-nosql-database-an-introduction.php

https://drive.google.com/open?id=1E3-abaRe0dSBiGCrJJqJ9hx-1P5HDDgL

<<tip>>  compare to ehcache, internal server caches ( session and global )

Redis (Remote DIctionary Server) is an open source, networked, single threaded, in- memory, advanced key-value store with optional durability. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.

★ Developer(s) : Salvatore Sanfilippo, Pieter Noordhuis
★ Initial release : April 10, 2009
★ Stable release : 2.8.12 / June 23, 2014
★ Development status : Active
★ Written in : ANSI C
★ Operating system : Cross-platform
★ Type : Key–value stores
★ License : BSD

Redis : What for?

★ Cache out-of-process
★ Duplicate detector
★ LIFO/FIFO Queues
★ Priority Queue
★ Distributed HashMap
★ UID Generator
★ Pub/Sub
★ Real-time analytics & chat apps
★ Counting Stuff
★ Metrics DB
★ Implement expires on items
★ Leaderboards (game high scores)
★ Geolocation lookup
★ API throttling (rate-limits)
★ Autocomplete
★ Social activity feed

Who's using Redis?

★ Twitter
★ Github
★ Weibo
★ Pinterest
★ Snapchat
★ Craigslist
★ Digg
★ StackOverflow
★ Flickr
★ 220.000.000.000 commands per day
★ 500.000.000.000 reads per day
★ 50.000.000.000 writes per day
★ 500+ servers 2000+ Redis instances!

key Features

★ Atomic operations
★ Lua Scripting
★ Pub/Sub
★ Transactions
★ Master/Slave replication
★ Cluster (with automatic sharding)*
★ Automatic failover (Redis Sentinel)
★ Append Only File (AOF) persistence
★ Snapshot (RDB file) persistence
★ Redis can handle up to 232 keys, and was tested in practice to handle at
least 250 million of keys per instance.
★ Every list, set, and sorted set, can hold 232 elements.

key Advantages & Disadvantages

► Advantages

★ Blazing Fast
★ Robust
★ Easy to setup, Use and maintain
★ Extensible with LUA scripting

► Disadvantages

★ Persistence consumes lot of I/O when using RDB
★ All your data must fit in memory

Redis in-memory

► Redis is an in-memory but persistent on disk database, so it represents a different trade off where very high write and read speed is achieved with the limitation of data sets that can't be larger than memory.

Why GraphQL

Fits CQRS - Command Query Request Separation Principle for performance, modularity, control

Glide - open-source GraphQL front-end for Salesforce API set

https://postlight.com/trackchanges/introducing-glide-graphql-for-salesforce

Glide is a free toolkit for engineers who are building Salesforce integrations into their apps. Glide allows you to seamlessly execute GraphQL requests against any Salesforce instance with minimal setup. The code is free and open source. Get Glide on GitHub.

Developers love working with GraphQL, because it brings a strongly-typed schema to your application’s data, and gives clients control over requesting exactly what they need, no more and no less. This reduces network traffic (and latency) compared to REST, where your app has to make multiple calls to various endpoints to retrieve the data it needs (plus some it doesn’t). When GraphQL is paired with React, it’s an incredible development toolchain.

Glide is the quickest way to prototype and build GraphQL applications backed by Salesforce data. Glide automatically introspects your Salesforce data models, and creates an intuitive and idiomatic GraphQL schema out of the box. Glide also handles Salesforce authentication for you, so you can hit the ground running and start prototyping right away.

Glide can spin up a GraphQL server pointing at your Salesforce instance, or you can import Glide into your existing server code and deploy it. With Glide, you can start making GraphQL queries and mutations on your Salesforce data in two commands. Step-by-step instructions to get started are available in the docs.

Semantic Data Services and Data Models

Goals

Break down data silos, data translation mapping work

Foundation for mapping to smart contracts across disparate organizations

Data Services Catalog Service for a Context

Find, add, map new items to the catalog ( source can be a data store, message service, etc )

Client understands a data source semantically ( organization meaning vs simple schema attributes ).

theCustomerOrder, theCustomerOrderNbr vs simple   ORDNO, type packed decimal etc

Automated Curation, Data Certification ( eg Pyramis data feeds w suspended batches / transactions ) of source and data

Free Time Stamp Authority Service

https://www.freetsa.org/index_en.php

Trusted time stamping is the process of securely keeping track of the creation and modification times of a document. Security here means that no one - not even the owner of the document - should be able to change it once it has been recorded provided that the time stamper's integrity is never compromised. FreeTSA trusted time stamping Software as a Service (SaaS) provides an easy method to apply RFC 3161 trusted timestamps to time-sensitive transactions through independently verified and auditable date and UTC (Coordinated Universal Time) sources.

Adding a trusted timestamp to code or to an electronic signature provides a digital seal of data integrity and a trusted date and time of when the transaction took place. Recipients of documents and code with a trusted timestamp can verify when the document or code was digitally or electronically signed, as well as verify that the document or code was not altered after the date the timestamp vouches for. (Readme).

For multiple files, the general concept is that time stamping a single file that contains an aggregate list of fingerprints of other files, also proves that these other files must have existed before the aggregate file was created, provided that both the aggregate file and the referenced file are available during verification process. Freetsa also offers the possibility of URLs timestamps (do not abuse). If you are interested in implementing timestamps on your project / company using the FreeTSA service, you can contact me for specific requirements. Freetsa can also be used within the Tor anonymity network.

Making timestamps from Android/iOS devices is possible and no software installation is required (Video). To timestamp a picture you must go to the "Online Signature" section in the browser, click "Choose a file" and select "Use camera". Once the photo or video are completed, you can download the timestamp (TimeStampResponse). Just that. You also may select any other file available in the device if you choose so.

Freetsa.org offers free OCSP and CRL, NTP, DNSCRYPT, DNS (Port 53) and DNS over TLS (DoT) services for time synchronisation and encrypted name resolution respectively. The resolution of DNSCRYPT (Port 553) do not have any type of restriction (SPAM, Malware, Parental,...). No logs are saved. Like the rest of the services offered by FreeTSA, DoT accepts TCP connections to port 853 on all its IPv4/IPv6 addresses.

Guide: How to sign PDF documents files with time stamp
FreeTSA onion domain (Tor): th3ccojidpgbgv5d.onion (https /http).
RFC 3161 TSA: Time-Stamp Protocol (TSP).
RFC 958 NTP: Network Time Protocol (NTP).

Event-Driven Data Services concepts

Difference between REST API and Event Driven API 

REST APIs can be asynchronous using

1. web hooks - call backs
2. web sockets

Event-Driven APIs

generally designed to respond or handle a stream of events from clients

the simplified diagram from Gravitee below highlights the differences 

In reality, the request model for event-driven COULD be request / response ( not shown below ) OR pub / sub ( broadcast )

SSE - Server Sent Events - publish an event to a client listener

A number of databases now support SSE.

This is a server to client push technology for messages the client can respond to

Unlike web sockets that provides the same server push capability, SSE does not allow the client to respond directly to the message which may be a securit advantage

This was a very useful model when I needed to capture transaction data from vendor applications that refused to provide an agent to see those transactions

If you control the database directly, those enterprise databases can be configured to publish SSE to clients. If not, you can use the binary logs as I did below to generate SSE from a server app to clients.

Most enterprise databases ( MySQL, Postgres, DB2, Oracle, SQL Server etc ) have binary log files for federated or distributed clusters that can be read by a client app to capture the data transactions and publish those to remote clients listening for a SSE. You can define a responsive workflow that is initiated by a transaction in a proprietary app IF it uses one of these enterprise databases.

https://www.linkedin.com/posts/gravitee-io_book-a-demo-with-one-of-our-api-experts-today-activity-7161448046931771392-ECW0?utm_source=share&utm_medium=member_desktop

any enterprise Data Service ( database, service etc ) should be able to:

• generate events ( typically SSE - Server Sent Events to any registered client that is read-only event data 
• register to listen for events from other event sources including event frameworks like Apache EventMesh, Kafka etc
• a data service event handler would be to invoke a process ( eg gRPC, API or run a query ) typically

Using SSE ( server sent events ) generated by a service from SQL DB binary replicas to integrate with closed solutions like Cavu HCM

you may have a software vendor where you license their application software and you own your data but they won't provide a free API to access your data from their servers. Given 90% of those solutions use a standard database on the backend like ( DB2, MySQL, Mongo etc )  and the standard databases can update a replica using a binary log, the DB vendors have tools or apis to access the binary log file transactions.  Creating a function to read each selected binary log event, you can create SSE ( Server Sent Events ) to publish any selected events to registered listeners.  This may be an effective work around to integrate using event-driven automation even when your software vendor doesn't provide direct api or event access.

More on SSE

m Data Architecture#Data-Architecture-Support-for-Event-Streams-for-Async-Server-Data-Flow-with-SSE

https://en.wikipedia.org/wiki/Server-sent_events

https://shopify.engineering/server-sent-events-data-streaming

Event-Driven Services Concepts Terminology

https://developer.confluent.io/faq/apache-kafka/architecture-and-terminology/

Apache Projects for GEMS - Global Event Management Solutions

m Apache Data Services#ApachePulsar

FinTech common data models

CDM (Common Domain Model) from ISDA

https://www.isda.org/2019/03/20/isda-publishes-cdm-2-0-for-deployment-and-opens-access-to-entire-market/

CMD Fact Sheet

https://www.isda.org/a/z8AEE/ISDA-CDM-Factsheet.pdf

The ISDA CDM is the first industry solution to tackle the lack of standard conventions in how derivatives trade events and processes are represented. Developed in response to regulatory changes, high costs associated with current manual processes and a demand for greater automation across the industry, the ISDA CDM for the first time creates a common blueprint for events that occur throughout the derivatives lifecycle, paving the way for greater automation and efficiency at scale.

“The current derivatives infrastructure is hugely inefficient and costly, and there’s virtually no way to implement scalable automated solutions across the industry.

PURE data modeler with Alloy UI - GS

( see https://medium.com/@vipinsun/pure-alloy-2fd3cd995e90 for inspiration on Data Silos and Pure + Alloy )

Trust Services Extensions to Data Solutions

STS - Smart Trust Services: deliver trusted outcomes

Potential Value Opportunities

Smart Data Services = the right Data Value Services infrastructure 

Data Infrastructure Concepts - What good data looks like - datacamp

sdm-smart-data-mgt-datacamp-What Mature Data Infrastructure Looks Like.pdf

What valuable data looks like 

https://www.datacamp.com/resources/webinars/data-trends-2021

Smart Data Services Infrastructure

Core Smart Data Management Principles

EEP - Entry > Edit > Post

Data Quality = 

• data issue prevention  ( goalie )
• data issue cleanup   ( janitor )
• data understanding  ( whisperer )
• data satisfaction  ( therapist )
• data value  ( value provider )

Data Operations Management Model for Analytics and AI

https://www.linkedin.com/posts/rajkgrover_dataops-datamanagement-agile-activity-7066804921358032897-qRV9?utm_source=share&utm_medium=member_desktop

DataOps is a collaborative data management discipline that focuses on end-to-end data management and the elimination of data silos.

DataOps offers the following benefits:
 
•           Decreases the cycle time in deploying analytical solutions
•           Lowers data defects
•           Reduces the time required to resolve data defects
•           Minimizes data silos
 

Reactive Relational DB Connections  ( R2DBC ) and services example

https://dzone.com/articles/transactional-outbox-pattern-step-by-step-with-spr?edition=598292&utm_campaign=&utm_content=Writing%20a%20vector%20database%20in%20a%20week%20in%20Rust&utm_medium=email&utm_source=dzone&utm_term=

r2dbc-dzone.com-Transactional Outbox Patterns - DZone.pdf link

r2dbc-dzone.com-Transactional Outbox Patterns - DZone.pdf file

we need a Transactional Outbox is that a service often needs to publish messages as part of a transaction that updates the database. Both the database update and the sending of the message must happen within a transaction. Otherwise, if the service doesn’t perform these two operations automatically, a failure could leave the system in an inconsistent state.

The GitHub repository with the source code for this article.

In this article, we will implement it using Reactive Spring and Kotlin with Coroutines. Here is a full list of used dependencies: Kotlin with Coroutines, Spring Boot 3, WebFlux, R2DBC, Postgres, MongoDB, Kafka, Grafana, Prometheus, Zipkin, and Micrometer for observability.

Data Governance Opportunities

see STS - Smart Trust Services: deliver trusted outcomes for advanced concepts

m Data Architecture#Data-Governance-Management-Concepts

m Data Architecture#Data-Governance-Concepts-and-Tools

Data Governance Concepts

Linkedin - RajG on Data Governance concepts

Data governance has its roots in the field of information management and has evolved over time in response to the increasing importance of data in various industries. The concept of data governance emerged as organizations recognized the need to manage their data as a valuable asset and ensure its quality, security, and compliance. The history of data governance can be traced back to several key factors and developments:
 
- Early #InformationManagement
- IT and Database Management
- Regulatory and #Compliance Requirements
- Emergence of #DataWarehousing and #BusinessIntelligence
- Key US Regulations > Sarbanes-Oxley Act (SOX) and Dodd-Frank Act 
- #DataQuality and Trust
- Master Data Management (MDM) 
- Digital Transformation 
- Emergence of Big Data and #Analytics
- Risk Management
- Complexity and Diversity of Data Sources
- Data Privacy Concerns and #GDPR
- Digital Asset Tokenization and Digital Twins 
- Modern Data Governance
- Globalisation and Data Sharing
- Technology Advancement

Over time, these factors led to the formalization of data governance frameworks and practices within organizations. Today, data governance encompasses a range of activities, including defining data ownership, establishing data policies and standards, ensuring data quality, managing data access and security, complying with regulations, and aligning data initiatives with business goals.
 
Overall, data governance has evolved from a response to challenges posed by data management and regulatory compliance into a strategic discipline that enables organizations to maximize the value of their data while managing risks and ensuring accountability.

a>> Data Governance areas for VSLN

Potential Challenges

Candidate Solutions & Tools

Kafka & Spark - distributed event message network with streaming support

Kafka - distributed messaging service for message brokers and pub sub use cases

Spark - data transformation services library with data streaming support

Apache Events

EventMesh - global event mesh network supporting local and remote events for global workflows

Apache Pulsar - distributed global event mesh

CouchDB - NoSQL Distributed database with built-in API and Logical Query language - open-source

LevelDB - Key Value distributed database - open-source

CockroachDB - Key Value distributed database

https://alternativeto.net/software/cockroachdb/

YugabyteDB - Cloud native DB w Postgres SQL, Query Layer, Storage Layer, Replicas
( and Processes? Trusts? Events? Interfaces? Extensions? Mapping? Directories ? )

https://en.wikipedia.org/wiki/YugabyteDB

YugabyteDB is a high-performance transactional distributed SQL database for cloud-native applications, developed by Yugabyte.^[1]

data-Distributed SQL Databases for Dummies-Yugabyte.pdf. link

Architecture

YugabyteDB is a distributed SQL database that aims to be strongly transactionally consistent across failure zones (i.e. ACID compliance].^[22][23] Jepsen testing, the de facto industry standard for verifying correctness, has never fully passed, mainly due to race conditions during schema changes.^[24] In CAP Theorem terms YugabyteDB is a Consistent/Partition Tolerant (CP) database.^[25][26][27]

YugabyteDB has two layers,^[28] a storage engine known as DocDB and the Yugabyte Query Layer.^[

YugabyteDB has the ability to replicate between database instances.^[47][48] The replication can be one-way or bi-directional and is asynchronous

DocDB Storage Services

The storage engine consists of a customized RocksDB^[29][30] combined with sharding and load balancing algorithms for the data. In addition, the Raft consensus algorithm controls the replication of data between the nodes.^[29][30] There is also a Distributed transaction manager^[29][30] and Multiversion concurrency control (MVCC)^[29][30] to support distributed transactions.^[30]

The engine also exploits a Hybrid Logical Clock^[31][29] that combines coarsely-synchronized physical clocks with Lamport clocks to track causal relationships.^[32]

The DocDB layer is not directly accessible by users.^[29

YQL = Y Query Layer

Yugabyte has a pluggable query layer that abstracts the query layer from the storage layer below.^[33] There are currently two APIs that can access the database:^[30]

YSQL^[34] is a PostgreSQL code-compatible API^[35][36] based around v11.2. YSQL is accessed via standard PostgreSQL drivers using native protocols.^[37] It exploits the native PostgreSQL code for the query layer^[38] and replaces the storage engine with calls to the pluggable query layer. This re-use means that Yugabyte supports many features, including:

• Triggers & Stored Procedures^[36]
• PostgreSQL extensions that operate in the query layer^[36]
• Native JSONB support^[36]

YCQL^[39] is a Cassandra-like API based around v3.10 and re-written in C++. YCQL is accessed via standard Cassandra drivers^[40] using the native protocol port of 9042. In addition to the 'vanilla' Cassandra components, YCQL is augmented with the following features:

• Transactional consistency - unlike Cassandra, Yugabyte YCQL is transactional.^[41]
• JSON data types supported natively^[42]
• Tables can have secondary indexes^[43]

Currently, data written to either API is not accessible via the other API, however YSQL can access YCQL using the PostgreSQL foreign data wrapper feature.^[44]

The security model for accessing the system is inherited from the API, so access controls for YSQL look like PostgreSQL,^[45] and YCQL looks like Cassandra access controls.

YugabyteDB versions

Step-by-step guide for Example

sample code block

Recommended Next Steps

Related articles