Alibaba Cloud Independent Software Vendors (ISVs) is here to empower your digital transformation. In collaboration with industry-leading partners such.

By Jessie Angelica, Solution Architect

Alibaba Cloud Independent Software Vendors (ISVs) is here to empower your digital transformation. In collaboration with industry-leading partners such as Confluent, IBM Qradar, Neo4j, and Sensors Data. Alibaba Cloud proudly presents a curated selection of transformative technologies designed to elevate Various Sector Industry to new heights. As a leading global provider of cloud computing and data intelligence services, we are dedicated to empowering businesses with the tools they need to thrive in an era of digital transformation.

Our collaboration with esteemed ISV underscores the importance of strategic partnerships in driving digital success. By combining the strengths of Alibaba Cloud's secure and scalable cloud platform with the specialized expertise of our partners, we offer a comprehensive suite of solutions that address the unique challenges faced by Various Sector Industry in today's dynamic landscape.

This Blog is more than a showcase of products; it represents a commitment to excellence, showcasing innovative solutions seamlessly integrating with Alibaba Cloud's robust infrastructure. Explore the possibilities that arise from the seamless integration of Alibaba Cloud's cutting-edge technologies with the specialized solutions offered by our ISV partners.

Confluent

confluent_image

Industry : FSI

ISV Solution : Enterprise KAFKA Data Streaming

Use Case Scenario : Fraud Detection, Web Data Event Tracking

Customer Type : Banks & eWallet companies

Confluent is an company that provides a enterprise streaming platform based on Apache Kafka, which enables you to access, store, and manage data as continuously, in real-time streams. It extends Kafka by providing additional tools, services, and capabilities to make it easier for organizations to build and manage their event streaming applications.

Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications.

Competitiveness : Cloudera, Striim, EvetadorLabs

The difference between Apache Kafka and Confluent

Apache Kafka:

a) Advantages:

• Open-source: Kafka is freely available and has a large community of users and contributors.
• Scalability: Kafka is designed to scale horizontally to handle large amounts of data and high-throughput requirements.
• Durability: Kafka provides fault-tolerance and durability for data by replicating it across multiple nodes.

b) Disadvantages:

• Complexity: Setting up and managing Kafka clusters can be complex, especially for users who are new to distributed systems.

Confluent:

a) Advantages:

• Enterprise Support: Confluent offers enterprise-level support and services, with additional tools and services, such as Confluent Platform, which includes Schema Registry, Kafka Connect, and Control Center.

• Fully-Managed Streaming Service: Confluent simplify the operational aspects of Kafka, making it more accessible to a broader audience.

• High Cluster Scalability and Performance: Ensure no SPoF (single point of failure) in system such as apps lag, downtime.

b) Disadvantages:

• Cost: Confluent's additional features and support come at a cost.

Workflow:

Data Ingestion:

Apache Kafka responsible for serves as the core event streaming platform for ingesting and processing real-time data, such as user interactions, website events, and transaction data.

Schema Evolution:

The Schema Registry ensures that changes in data schemas such as new features or data attributes are managed seamlessly and critical for maintaining consistency in data formats to ensure compatibility over time.

Integration with External Systems:

Kafka Connect is used to integrate with external systems, such as a customer relationship management (CRM) system, a recommendation engine, and a data warehouse. As well as, Kafka connect could be used to connect with various sources like databases, cloud storage, and other third-party systems.

Real-time Analytics:

Kafka Streams is employed for real-time stream processing. Analytics applications consume the data streams, perform computations, and generate real-time insights.

Monitoring and Management:

Control Center is used to monitor the health and performance of Kafka clusters. The operations teams can receive alerts, track data flow, and ensure that the system is operating efficiently.

Use Case :

Fraud Detection:

There are people who try to commit fraud. The Kafka Streams can identify suspicious financial patterns by ingest real-time data from various sources, such as transaction logs, user activity, and external data feeds, into Kafka topics. Kafka's event time will analyze the events based on their timestamp. Detected fraud patterns can trigger alerts or automated responses in real time, helping financial institutions respond swiftly to potential threats.

Web Data Event Tracking

A bank receiving lots of data transactions every day via websites/apps. The users interact with the digital banking platform by logging in, checking account balances, transferring funds, and performing other financial transactions. All of the activities are tracked and sent to Kafka topics in real-time to prevent fraud in financial transactions.

IBM Qradar

ibm_image

Industry : FSI

ISV Solution : SIEM (Security Information and Event Management)

Use Case Scenario : Advanced Threat Detection, Ransomware

Customer Type : Banks

IBM Qradar is Security Information and Event Management (SIEM) solution designed to detect, investigate and respond to cyber-security threats in real time. It enables your security system to quickly detect anomalies and cyber-attacks while eliminating many false positives.

How It Works?

IBM QRadar works by collecting, normalizing, and storing log and event data from various sources.

It uses correlation rules and algorithms to analyze the data for patterns indicative of security threats.

The platform employs a combination of signature-based detection, anomaly detection, and behavioral analysis.

When a security event is detected, QRadar generates alerts and provides tools for security analysts to investigate and respond to the incident.

QRadar offers customizable dashboards and reporting capabilities that enable organizations to visualize and communicate their security and compliance.

Use Case :

A company definitely want to protect customers' identities, protect property, and avoid attacks. So company doesn't need to constantly monitor the system because Qradar helps detect threats by applying automatic security intelligence. QRadar collects and analyzes logs and events in real-time from diverse sources, including endpoint, network, apps, etc, and responding by prioritizing threats from the most dangerous before attackers cause material damage.

Benefits

Alibaba Cloud Integration
Collects and aggregates log and event data from on-premise, hybrid, and multi-cloud environments ((600+ validated integrations)

Real-Time Threat Detection

Distinguishing normal behavior from malicious activities correlated to data to identify patterns and anomalies that may indicate potential security threats. (51% increase detection)

Incident Response

Allows for the automation of response actions, such as blocking suspicious IP addresses, isolating affected systems, and generate the alert. (8x increase respond)

Manage compliance

Predefined reports and templates that align with various compliance standards to meet regulatory requirements

Monitor OT and IoT security

By integrating with specialized protocols and technologies commonly used in the domains.

Neo4j

NEO4J_IMAGE

Industry: FSI

ISV Solution: Graph Database

a) Use Case Scenario : Fraud Detection Risk Control , AML Customer Type : Banks
b) Use Case Scenario : Recommendation Personalization Customer Type : eCommerce
c) Use Case Scenario : Knowledge Graph , Prod Data Management Customer Type : Supply Chain

Neo4j Graph Database is a database management system that is designed to store, query, and analyze highly interconnected data. Unlike traditional relational databases, graph databases are optimized for handling relationships and connections between entities. Neo4j uses a property graph model where data is represented as nodes, relationships, and key-value properties. It is using real-time transaction processing, advanced AI/ML, intuitive data visualization, and more.

How It Works?

Graph databases organize data using nodes, relationships, and properties. Nodes represent entities, relationships represent connections between nodes, and properties are key-value pairs associated with nodes and relationships. When querying a graph database, the relationships are traversed to find relevant nodes.

Graph Database’s output is presented in tabular form, consists of include nodes, relationships, properties, or other relevant information from the graph. The investigation can be further initiated for flagged accounts to assess the risk and take appropriate action, such as freezing accounts and conducting additional KYC (Know Your Customer) checks.

Use Case

Products Featured (Retail and insurance)

A graph-based recommendation algorithm is an algorithm that can recommend products or services to customers based on similar factors (eg: age, gender, income, spending) with other customers who have similar characteristics. for example, if several customers have similar characteristics and they buy a certain brand of car, Neo4j can recommend the same brand to other customers who have similar characteristics.

Financial Risk Control (Financial Service)

A financial services provider wants to detect fraudulent activity between account holder/ relationships between entities. How many suspected accounts are in the applicant's who are indirectly connected through a network of transactions and connections within a distance of 3 or more connections. Neo4j can detect potential fraud and potential money laundering activities by identify suspicious patterns and unusually high transaction volumes.

Manufacturing Service

Neo4j can help in modeling how inventory items relate to each other through how certain items are used in various products and manufacturing processes. Example: Identifying inventory items that have high cross-dependencies and optimizing supply. Neo4j can analyze how inventory items are used in the production of specific products / multiple products / manufacturing processes. By running queries, you can identify inventory items that have high cross-dependencies across various products and manufacturing processes.

Sensors Data

Industry : Retail

ISV Solution : Sensor Digital Operation &Marketing Solutions

Use Case Scenario : Customer Management, A/B Testing

Customer Type : eCommerce

Sensors softwares can provide a one-stop platform for big data analysis and operation, can help enterprises to achieve full digitalization of operation and marketing process.

Data analysts can explore key behaviors and gain insights into the various growth. The product can merge multi-source data, identify unique users and help enterprises build a labeling and portrait system to enable businesses to achieve refined user operations and precise marketing. The product Provides an automated operation platform to achieve precise reach of target groups and improve key indicators and operational efficiency.

Three major functions to fully meet the needs of enterprises' digital operation and marketing.

Sensors Analytics

Sensors Analytics a product analysis platform to helps businesses gain insights into user behavior, preferences, and interactions with digital platforms or products.

Example: In an e-commerce website, sensor analytics can track user clicks, and see how their sales are going by analyze sales data by understanding sales trends, the most popular products, how many orders and cancellations, etc.

Sensors Personas

Sensors Persona is a user analysis platform to analyze the characteristics, needs, and behaviors of different customer groups by clustering and segmentation

Example: For ecommerce, personas helps companies understand their customers, starting from the characteristics and preferences they like, the areas with the most high demands, gender, age, whether the customer is “new users," “active users," or “loyal users," each requiring different marketing approaches.

Sensors Focus

Sensors Focus is focusing on key performance indicators (KPIs) derived from sensor data, businesses can refine their marketing strategies and operational efforts to achieve specific objectives.

Example: Ecommerce can dentifying specific target customers who often shop on e-commerce and sending special offers only to them to save unnecessary advertising costs.

Product Service

Use Case

Gaming Industry
Digital Sensors can understand how players behave and what they like in games so that game companies can create games with more interesting features and increase revenue

Retail Industry

Digital sensors can analyze by offering products and services that are more suited to customer needs, thereby increasing customer satisfaction and business growth.

Benefits

● Global Data Integration
The product can provide multi-terminal collection based on SDKs and data interconnection, which supports unified user identification and business data integration.

● Professional Analytics
The product can provide analytics models, metrics system and user label system to identifying trends, measuring user engagement levels, and creating advertising strategies

● Business Enablement
The product can achieve refined operations on users, data-driven product intelligence and data-driven decision-making by identify areas where efficiency can be improved

● Cloud-Native Infrastructure
The product is based on the on-cloud auto scaling and global data management services, which reduce O&M costs.

Community

Exploring Top 4 Alibaba Cloud ISV Solutions in Diverse Industries

Confluent

The difference between Apache Kafka and Confluent

Apache Kafka:

Confluent:

Workflow:

Use Case :

IBM Qradar

How It Works?

Use Case :

Benefits

Neo4j

How It Works?

Use Case

Sensors Data

Use Case

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