How to Analyze Data with Metabase? A Comparison To 5 Most Popular Analytical Services

Apache Kafka is a high-performance server-based message broker capable of processing enormous volumes of events, measured in millions per second. Kafka's distinctive features include exceptional fault tolerance, the ability to store data for extended periods, and ease of infrastructure expansion through the simple addition of new nodes. The project's development began within LinkedIn, and in 2011, it was transferred to the Apache Software Foundation. Today, Kafka is widely used by leading global companies to build scalable, reliable data transmission infrastructure and has become the de facto industry standard for stream processing. Kafka solves a key problem: ensuring stable transmission and processing of streaming data between services in real time. As a distributed broker, it operates on a cluster of servers that simultaneously receive, store, and process messages. This architecture allows Kafka to achieve high throughput, maintain operability during failures, and ensure minimal latency even with many connected data sources. It also supports data replication and load distribution across partitions, making the system extremely resilient and scalable. Kafka is written in Scala and Java but supports clients in numerous languages, including Python, Go, C#, JavaScript, and others, allowing integration into virtually any modern infrastructure and use in projects of varying complexity and focus. How the Technology Works To work effectively with Kafka, you first need to understand its structure and core concepts. The system's main logic relies on the following components: Messages: Information enters Kafka as individual events, each representing a message. Topics: All messages are grouped by topics. A topic is a logical category or queue that unites data by a specific characteristic. Producers: These are programs or services that send messages to a specific topic. Producers are responsible for generating and transmitting data into the Kafka system. Consumers: Components that connect to a specific topic and extract published messages. To improve efficiency, consumers are often organized into consumer groups, thereby distributing the load among different instances and allowing better management of parallel processing of large data volumes. This division significantly improves overall system performance and reliability. Partitions: Any topic can be divided into partitions, enabling horizontal system scaling and increased performance. Brokers: Servers united in a Kafka cluster perform functions of storing, processing, and managing messages. The component interaction process looks as follows: The producer sends a message to a specified topic. The message is added to the end of one of the topic's partitions and receives its sequential number (offset). A consumer belonging to a specific group subscribes to the topic and reads messages from partitions assigned to it, starting from the required offset. Each consumer independently manages its offset, allowing messages to be re-read when necessary. Thus, Kafka acts as a powerful message delivery mechanism, ensuring high throughput, reliability, and fault tolerance. Since Kafka stores data as a distributed log, messages remain available for re-reading, unlike many queue-oriented systems. Key Principles Append-only log: messages are not modified/deleted (by default), they are simply added. This simplifies storage and replay. Partition division for speed: one topic is split into parts, and Kafka can process them in parallel. Thanks to this, it scales easily. Guaranteed order within partition: consumers read messages in the order they were written to the partition. However, there is no complete global ordering across the entire topic if there are multiple partitions. Messages can be re-read: a consumer can "rewind" at any time and re-read needed data if it's still stored in Kafka. Stable cluster operation: Kafka functions as a collection of servers capable of automatically redirecting load to backup nodes in case of broker failure. Why Major Companies Choose Apache Kafka There are several key reasons why large organizations choose Kafka: Scalability Kafka easily handles large data streams without losing performance. Thanks to the distributed architecture and message replication support, the system can be expanded simply by adding new brokers to the cluster. High Performance The system can process millions of messages per second even under high load. This level of performance is achieved through asynchronous data sending by producers and efficient reading mechanisms by consumers. Reliability and Resilience Message replication among multiple brokers ensures data safety even when part of the infrastructure fails. Messages are stored sequentially on disk for extended periods, minimizing the risk of their loss. Log Model and Data Replay Capability Unlike standard message queues where data disappears after reading, Kafka stores messages for the required period and allows their repeated reading. Ecosystem Support and Maturity Kafka has a broad ecosystem: it supports connectors (Kafka Connect), stream processing (Kafka Streams), and integrations with analytical and Big Data systems. Open Source Kafka is distributed under the free Apache license. This provides numerous advantages: a huge amount of official and unofficial documentation, tutorials, and reviews; a large number of third-party extensions and patches improving basic functionality; and the ability to flexibly adapt the system to specific project needs. Why Use Apache Kafka? Kafka is used where real-time data processing is necessary. The platform enables development of resilient and easily scalable architectures that efficiently process large volumes of information and maintain stable operation even under significant loads. Stream Data Processing When an application produces a large volume of messages in real time, Kafka ensures optimal management of such streams. The platform guarantees strict message delivery sequence and the ability to reprocess them, which is a key factor for implementing complex business processes. System Integration For connecting multiple heterogeneous services and applications, Kafka serves as a universal intermediary, allowing data transmission between them. This simplifies building microservice architecture, where each component can independently work with event streams while remaining synchronized with others. Data Collection and Transmission for Monitoring Kafka enables centralized collection of logs, metrics, and events from various sources, which are then analyzed by monitoring and visualization tools. This facilitates problem detection, system state control, and real-time reporting. Real-Time Data Processing Through integration with stream analytics systems (such as Spark, Flink, Kafka Streams), Kafka enables creation of solutions for operational analysis and rapid response to incoming data. This allows for timely informed decision-making, formation of interactive monitoring dashboards, and instant response to emerging events, which is critically important for applications in finance, marketing, and Internet of Things (IoT). Real-Time Data Analysis Through interaction with stream analytics tools (for example, Spark, Flink, Kafka Streams), Kafka becomes the foundation for developing solutions ensuring fast processing and analysis of incoming data. This functionality enables timely important management decisions, visualization of indicators in convenient interactive dashboards, and instant response to changing situations, which is extremely relevant for financial sector companies, marketers, and IoT solution developers. Use Case Examples Here are several possible application scenarios: Web platforms: any user action (view, click, like) is sent to Kafka, and then these events are processed by analytics, recommendation system, or notification service. Fintech: a transaction creates a "payment completed" event, which the anti-fraud service immediately receives. If suspicious, it can initiate a block and pass data further. IoT devices: thousands of sensors send readings (temperature, humidity) to Kafka, where they are processed by streaming algorithms (for example, for anomaly detection), and then notifications are sent to operators. Microservices: services exchange events ("order created," "item packed," etc.) through Kafka without calling each other directly. Log aggregation: multiple services send logs to Kafka, from where analytics systems, SIEM, or centralized processing systems retrieve them. Logistics: tracking delivery statuses or real-time route distribution. Advertising: collection and analysis of user events for personalization and marketing analytics. These examples demonstrate Kafka's flexibility and its application in various areas. When Kafka Is Not Suitable It's important to understand the limitations and situations when Kafka is not the optimal choice. Several points: If the data volume is small (for example, several thousand messages per day) and the system is simple, implementing Kafka may be excessive. For low traffic, simple queues like RabbitMQ are better. If you need to make complex queries with table joins, aggregations, or store data for very long periods with arbitrary access, it's better to use a regular database. If full ACID transactions are important (for example, for banking operations with guaranteed integrity and relationships between tables), Kafka doesn't replace a regular database. If data hardly changes and doesn't need to be quickly transmitted between systems, Kafka will be excessive. Simple storage in a database or file may be sufficient. Kafka's Differences from Traditional Databases Traditional databases (SQL and NoSQL) are oriented toward storing structured information and performing fast retrieval operations. Their architecture is optimized for reliable data storage and efficient extraction of specific records on demand. In turn, Kafka is designed to solve different tasks: Working with streaming data: Kafka focuses on managing continuous data streams, while traditional database management systems are designed primarily for processing static information arrays. Parallelism and scaling: Kafka scales horizontally through partitions and brokers, and is designed for very large stream data volumes. Databases (especially relational) often scale vertically or with horizontal scaling limitations. Ordering and stream: Kafka guarantees order within a partition and allows subscribers to read from different positions, jump back, and replay. Latency and throughput: Kafka is designed to provide minimal delays while simultaneously processing enormous volumes of events. Example Simple Python Application for Working with Kafka If Kafka is not yet installed, the easiest way to "experiment" with it is to install it via Docker. For this, it's sufficient to create a docker-compose.yml file with minimal configuration: version: "3" services: broker: image: apache/kafka:latest container_name: broker ports: - "9092:9092" environment: KAFKA_NODE_ID: 1 KAFKA_PROCESS_ROLES: broker,controller KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093 KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092 KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT KAFKA_CONTROLLER_QUORUM_VOTERS: 1@localhost:9093 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1 KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1 KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0 KAFKA_NUM_PARTITIONS: 3 Run: docker compose up -d Running Kafka in the Cloud In addition to local deployment via Docker, Kafka can be run in the cloud. This eliminates unnecessary complexity and saves time. In Hostman, you can create a ready Kafka instance in just a few minutes: simply choose the region and configuration, and the installation and setup happen automatically. The cloud platform provides high performance, stability, and technical support, so you can focus on development and growth of your project without being distracted by infrastructure. Try Hostman and experience the convenience of working with reliable and fast cloud hosting. Python Scripts for Demonstration Below are examples of Producer and Consumer in Python (using the kafka-python library), the first script writes messages to a topic and the other reads. First, install the Python library: pip install kafka-python producer.py This code sends five messages to the test-topic theme. from kafka import KafkaProducer import json import time # Create Kafka producer and specify broker address # value_serializer converts Python objects to JSON bytes producer = KafkaProducer( bootstrap_servers="localhost:9092", value_serializer=lambda v: json.dumps(v).encode("utf-8"), ) # Send 5 messages in succession for i in range(5): data = {"Message": i} # Form data producer.send("test-topic", data) # Asynchronous send to Kafka print(f"Sent: {data}") # Log to console time.sleep(1) # Pause 1 second between sends # Wait for all messages to be sent producer.flush() consumer.py This Consumer reads messages from the theme, starting from the beginning. from kafka import KafkaConsumer import json # Create Kafka Consumer and subscribe to "test-topic" consumer = KafkaConsumer( "test-topic", # Topic we're listening to bootstrap_servers="localhost:9092", # Kafka broker address auto_offset_reset="earliest", # Read messages from the very beginning if no saved offset group_id="test-group", # Consumer group (for balancing) value_deserializer=lambda v: json.loads(v.decode("utf-8")), # Convert bytes back to JSON ) print("Waiting for messages...") # Infinite loop—listen to topic and process messages for message in consumer: print("Received:", message.value) # Output message content These two small scripts demonstrate basic operations with Kafka: publishing and receiving messages. Conclusion Apache Kafka is an effective tool for building architectures where key factors are event processing, streaming data, high performance, fault tolerance, and latency minimization. It is not a universal replacement for databases but excellently complements them in scenarios where classic solutions cannot cope. With proper architecture, Kafka enables building flexible, responsive systems. When choosing Kafka, it's important to evaluate requirements: data volume, speed, architecture, integrations, ability to manage the cluster. If the system is simple and loads are small—perhaps it's easier to choose a simpler tool. But if the load is large, events flow continuously, and a scalable solution is required, Kafka can become the foundation. Despite certain complexity in setup and maintenance, Kafka has proven its effectiveness in numerous large projects where high speed, reliability, and working with event streams are important.

VMware Cloud Director (formerly vCloud Director, or “vCD”) is a modern solution for cloud providers, mainly designed for building virtual data centers on top of physical infrastructure. The platform allows combining all of a data center’s physical resources into virtual pools, which are then offered to end users on a rental basis. It integrates tightly with VMware’s own technologies: vCenter and vSphere. vCenter is a set of tools for managing virtual infrastructure, and vSphere is the virtualization platform for cloud computing. Key Capabilities of VMware Cloud Director Creation of virtual data centers (vDCs) with full isolation of virtual services and resources. Migration of virtual machines (VMs) between clouds, and self-deployment of OVF templates. Snapshots and rollback of VM changes. Creation of isolated and routable networks with external access. Integrated, tiered storage with load balancing between virtual machines. Network security: perimeter protection and firewalling. Encryption of access to cloud resources to secure the virtual infrastructure. Unified authentication across all VMware services (single sign-on) so users don’t need to re-authenticate. Deployment of multi‑tier applications as ready-made virtual appliances, with VMs and OS images. Allocation of isolated resources for different departments within a single virtual structure. How VMware Cloud Director Works VMware Cloud Director uses a multi-tenant model. Rather than building a dedicated environment for every customer, it creates a shared virtual environment. This reduces infrastructure maintenance costs massively: for large cloud providers, savings can reach hundreds of thousands or even millions of dollars per year, which in turn lowers the rental cost for end users. Resource consumption model: Using vCenter and vSphere, the provider aggregates physical resources into a shared pool called a “virtual data center” (vDC). From that pool, resources are allocated into Org vDCs (Organizational Virtual Data Centers), which are the fundamental compute units consumed by customers. VMware Cloud Director syncs with the vSphere database to request and allocate the required amount of resources. Org vDCs are containers of VMs and can be configured independently. Customers can order different numbers of Org vDCs for different purposes, e.g., one Org vDC for marketing, another for finance, a third for HR. At the same time, interconnectivity can be established between these Org vDCs, forming a large, virtual private data center. It’s also possible to combine Org vDCs into multiple networks. Additionally, within those networks, one can create vApps (virtual applications) made up of VMs, each with their own gateways to connect to Org vDCs. This setup allows building virtual networks of any architecture, isolated or routable, to match various business needs. When such a network is created, the provider assigns a user from the customer organization to the role of network administrator. A unique URL is also assigned to each organization. The administrator is responsible for adding or removing users, assigning roles and resources, creating network services, and more. They also manage connections to services provided by the cloud provider. For instance, VM templates or OVF/OVA modules, which simplify backup and VM migration. Resource Allocation Models in VMware Cloud Director VMware Cloud Director supports several models for allocating resources, depending on how you want to manage usage: Allocation Pool: You set resource limits and also define a guaranteed percentage of the shared pool for a user. This  model is good when you want predictable costs but don’t need full reservation. Pay-As-You-Go: No guaranteed resources, only consumption-based; ideal if usage is variable. The model is flexible and fits users who want to grow gradually. Reservation Pool: You reserve all available resources; user requests are limited only by what the provider’s data center can supply. Reservation Pool is suited for organizations that need fixed performance and large infrastructure. Useful Features of VMware Cloud Director Here are several powerful features that optimize resource usage, routing, and tenant isolation: Delegation of Privileges You can assign network administrators from the users of each organization. These admins get broad rights: they can create and manage VMs, deploy OVF/OVA templates, manage VM migration, set up isolated/routable networks, balance VM workloads, and more. Monitoring and Analytics Cloud Director includes a unified system for monitoring and analyzing VM infrastructure: VMs, storage, networks, memory. All data is logged and visualized in a dedicated dashboard, making it easier to detect and resolve problems proactively. Networking Features Networking in vCloud Director supports dynamic routing, distributed firewalls, hybrid cloud integration, and flexible traffic distribution. Many of these features are now standard in the newer versions of Cloud Director. If you don’t already have some of them, you may need to upgrade your NSX Edge and convert it to an Advanced Gateway in the UI. Dynamic routing improves reliability by eliminating manual route configuration. You can also define custom routing rules based on IP/MAC addresses or groups of servers. With NSX Edge load balancing, incoming traffic can be distributed evenly across pools of VMs selected by IP, improving scalability and performance. Access Control and More You can create custom user roles in the Cloud Director UI to control access tailored to organizational needs. VMs can be pinned to specific ESXi host groups (affinity rules), which helps with licensing or performance. If Distributed Resource Scheduler (DRS) is supported, Cloud Director can automatically balance VMs across hosts based on load. Additional useful features include automatic VM discovery and import, batch updating of server cluster cells, and network migration tools.

Today, up to 100% of startups begin operating based on providers offering services ranging from simple virtual hosting to dedicated servers. In this article, we will examine the advantages of cloud computing that have led to its dominance over the “classic” approach of having a dedicated server in a separate room. Cloud Use Cases Typical scenarios for using cloud technologies include: Full migration of a business application to a remote server. For example, enterprise resource planning or accounting software. These applications support operation via remote desktop interfaces, thin clients, or web browsers. Migration of specific business functions. Increasingly, archival copies are stored in the cloud while software continues running locally. Alternatively, a backup SQL server node can be hosted remotely and connected in case the local server fails. Implementation of new services. Businesses are increasingly adopting automated systems for data collection and analytics. For example, Business Intelligence (BI) technologies have become popular, helping generate current and comparative reports. Interaction between local and cloud environments. Hybrid services are well established in large networks. For example, a retail store may operate a local network with an on-site server, receive orders from an online store, and send requests back to transport companies, and so on.This setup allows offline operation even if the internet is fully disconnected: processing sales, receiving shipments, conducting inventories, with automatic synchronization once connectivity is restored. These examples represent foundational scenarios, giving developers plenty of room to innovate. This is one reason more and more coders are attracted to the cloud. Advantages Now let’s examine the advantages and disadvantages of cloud computing. Yes, the technology has some drawbacks, including dependency on internet bandwidth and somewhat higher requirements for IT specialists. Experienced professionals may need retraining, whereas younger personnel who learn cloud technologies from the start do not face such challenges. Speed Software development often requires significant time and effort for application testing. Applications must be verified across multiple platforms, resolutions, and device types. Maintaining local machines dedicated to testing is inefficient. Cloud computing solves this by enabling rapid deployment of virtually any environment, isolated from other projects, ensuring it does not interfere with team development. High deployment speed and access to cloud services also encourage IT startups to launch almost “from scratch,” with minimal resource investment. The advantages of cloud services are especially critical when development volumes periodically expand. Purchasing hardware consumes a developer’s most valuable resource: time. In the cloud, selecting a plan takes just a few minutes, and the setup of a remote host for specific tasks can begin immediately. Hardware resources on the remote server, such as CPU cores, memory, and storage, can also be easily adjusted. Security Building a private server is expensive. Besides the powerful machines, you will need backup power and internet lines, a separate room with air conditioning and fire protection, and security personnel to prevent unauthorized access. Cloud providers automatically provide all these features at any service level. Other security advantages include: Easier identity and access management (IAM). Higher reliability for continuous business operations. Protection against theft or seizure of storage devices containing sensitive data. On a cloud server, users cannot simply plug in a USB drive to download files. Data does not reside on local machines, and access is controlled according to company policy. Users only see what their role allows. This approach reduces the risk of viruses and accidental or intentional file deletion. Antivirus software runs on cloud platforms, and backups are automatically maintained. Cost Efficiency Purchasing server hardware is a major budget burden, even for large corporations. Before the cloud boom, this limited IT development. Modern developers often need test environments with unique infrastructure, which may only be required temporarily. Buying hardware for a one-time test is inefficient. Short-term rental of cloud infrastructure allows developers to complete tasks without worrying about hardware maintenance. Equipment costs directly impact project pricing and developer competitiveness, so cloud adoption is advantageous. Today, most software is developed for cloud infrastructure, at least with support for it. Maintenance, storage, and disposal costs for IT equipment also add up. Hardware becomes obsolete even if unused. This makes maintaining developer workstations for “simple” desktop software costly. Offloading this to a cloud provider allows developers to always work with the latest infrastructure. Convenience Another cloud advantage is ease of use. Cloud platforms simplify team collaboration and enable remote work. The platform is accessible from any device: desktop, laptop, tablet, or smartphone, allowing work from home, the office, or even a beach in Bali. Clouds have become a foundation for remote work, including project management. Other conveniences include: Easy client demonstrations: Developers can grant access and remotely show functionality, or run it on the client’s office computer without installing additional components. Quick deployment of standard solutions: Setting up an additional workstation takes only a few minutes, from registering a new user to their trial login. New developers can quickly join ongoing tasks. Easy role changes: In dynamic teams, personnel often switch between projects. Access to project folders can be revoked with a few clicks once a task is completed. This also applies to routine work: adding new employees, blocking access for former staff, or reassigning personnel. A single administrative console provides an overview of activity and simplifies version tracking, archiving, and rapid deployment during failures. Stability Another factor affecting developer success is the speed of task completion. Beyond rapid deployment, system stability is critical. On local machines, specialists depend on hardware reliability. A failure could delay project timelines due to hardware replacement and configuration. Moving software testing to the cloud enhances the stability of local IT resources, particularly in hybrid systems. Cloud data centers provide Tier 3 minimum reliability (99.982% uptime) without additional client investment. Resources are pre-provisioned and ready for use according to the chosen plan. Development, testing, and operation are typically conducted within a single provider’s platform, in an environment isolated from client services. Conclusion Cloud technologies offer numerous advantages with relatively few drawbacks. Businesses and individual users value these benefits, and developers are encouraged to follow trends and create new, in-demand products. Virtually all commerce has migrated to the cloud, and industrial sectors, especially those with extensive branch networks and remote facilities, are also adopting cloud solutions.