Docker Complete Guide: All You Need to Know About Docker and Docker Containers

Infrastructure costs grow imperceptibly. Typically, teams start by renting a couple of virtual machines, a database, and storage. In this setup, the system works smoothly, and the team focuses on the product. But as the project grows, the infrastructure "sprawls": one provider for servers, another for databases, a third for file storage. Test environments, temporary instances, and "just in case" disks appear. As a result, the budget increases not because of new features, but because of numerous disparate solutions. The more complex the architecture becomes, the harder it is to control costs. The team spends time not on the product but on maintaining infrastructure, trying to maintain a balance between performance and budget. In this article, we'll explore how to approach cloud infrastructure rationally: what to optimize first, what we often overpay for, how to avoid fragmentation, and how to make the team’s life easier by consolidating key services on a single platform. Infrastructure Audit: What to Check First Cloud cost optimization doesn't start with cuts, but with transparency. Companies often try to save money without understanding where exactly the money is going. Therefore, the first step is to conduct an audit of the current infrastructure and identify inefficient or unused resources. To conduct a good audit, companies usually invite cloud architects or DevOps engineers. They typically look for problems according to the following plan. 1. Server Load The most common cause of unnecessary expenses is virtual machines launched "with reserve." If CPU and RAM consistently work at 10-20%, it means the configuration is excessive. This is especially noticeable in projects that scaled in a hurry and where resources were expanded just in case. It's useful to evaluate average and peak CPU load, the amount of RAM used, disk subsystem metrics like IOPS and latency, as well as network traffic dynamics—this provides a holistic understanding of how efficiently servers are working. In this case, even a small configuration adjustment can reduce costs without loss of stability. 2. Idle Resources Over time, infrastructure accumulates test servers, temporary databases, forgotten disks, and old snapshots. This is the invisible but constant expense item. Pay attention to virtual machines without traffic, disconnected disks, outdated backups, and test instances that were once launched temporarily but remained in the infrastructure. These are the elements that should be optimized in the first hours of the audit. 3. Databases Databases are one of the most expensive infrastructure components. Here, it's important to look not only at the number of resources, but also at the actual load. Often large clusters are deployed simply because "it's safer that way." It's useful to check query frequency, number of active connections, disk load, and the actual volume of storage used—these indicators will help quickly determine whether the current cluster size is justified. Also make sure databases aren't duplicated for different environments. 4. Logs and Storage Logs and media files can take up more and more space if they're not moved to object storage. Storing all this on server disks is unjustifiably expensive. Evaluate the volume of logs, their storage and rotation policy, media archive size, as well as backup location and frequency—this makes it easier to understand whether data is accumulating where it shouldn't be. Optimizing Compute Resources After the audit, it becomes clear which servers the project really needs and which work inefficiently. The next step is to select configurations so that they correspond to the actual load and grow with the product, rather than exceeding it several times over. The main principle here is that resources should not be "more than needed," but "exactly as much as needed now." If the load increases, in the cloud it's easier to add resources to an existing server or add a new instance than to constantly maintain a reserve for peak loads. This approach allows you to reduce costs without risk to stability. It's important to correctly choose machine types for different tasks. For example, standard VMs are most often suitable for web applications, GPU-optimized servers for analytical or ML workloads, and separate disk configurations for services with high read and write intensity. Another way to optimize cloud computing costs is not to scale up one large server, but to distribute the load across several smaller VMs using a load balancer. It receives incoming traffic and directs it to available instances so that no single machine becomes a "bottleneck." This approach scales smoothly: if the project grows, you simply add a new VM to the pool, and the balancer immediately takes it into account when distributing requests. In Hostman, the load balancer is built into the ecosystem and easily connects to any set of servers. When the load increases, the team spins up new instances; when it decreases, they shut down excess ones, thus adapting infrastructure to real conditions, not theoretical peaks. Ultimately, compute resource optimization is about flexibility. Resources scale with the product, and the budget is spent on what actually brings value, not on excessive configurations. Optimizing Database Operations After the audit, it becomes clear which database instances are actually used. The next step is to build a data storage architecture that is not only reliable but also economically justified. In working with databases, this largely depends on the correct choice of technology and operating model. Choosing a Database Engine Different types of loads require different approaches. Transactional systems—online stores, CRM, payment services—work best with classic OLTP (Online Transaction Processing) solutions like PostgreSQL or MySQL, where write speed and operation predictability are important. If we're talking about documents, user content, or flexible data schemas, MongoDB is more convenient. And analytical tasks—reports, metrics, aggregates over millions of rows—are better suited to OLAP (Online Analytical Processing) solutions like ClickHouse. The right database choice immediately reduces costs: the project doesn't overpay for resources that don't fit the load type and doesn't waste time on complex workarounds. Why DBaaS Saves Budget Even a perfectly selected database becomes expensive if you deploy and maintain it yourself. Administration, updates, replication, backup, fault tolerance—all this takes a lot of time and requires competencies that are difficult and expensive for startups or small teams to maintain. The DBaaS format removes most of these tasks. The platform provides SLA, monitors cluster fault tolerance, updates versions, manages backups, and provides clear scaling tools.  In addition, there are no hidden costs: the database works within a stable platform, and the provider takes on all infrastructure tasks. Horizontal Scaling Without Overpaying When the load grows, it doesn't always make sense to strengthen the main node. In managed databases, it's easier and more reliable to scale the system by distributing different types of load across separate services: leave the transactional part in the OLTP database and move analytical calculations to a separate OLAP cluster like ClickHouse. This approach reduces pressure on the main node and saves the application from slowdowns due to heavy queries. Within DBaaS, this is usually the most predictable and accessible scaling scenario—without manual sharding and complex replica configuration. This approach reduces pressure on the master node and allows avoiding a sharp budget jump. The system scales gradually: as the load grows, replicas are added rather than expensive "monolithic" server configurations. How to Save on Databases in Hostman Managed databases combine the convenience of DBaaS and configuration flexibility. Clusters are created in minutes, and configuration is selected based on project needs—without excessive reserve. When the load grows, you can increase the configuration. Scaling happens quickly and without complex migrations, and payment is only for actual resource consumption. This approach helps keep the budget under control and not overpay for capacity that is only partially used. File and Log Storage: Transition to Object Storage When a project grows, file volume inevitably increases: media, exports, backups, temporary data, system artifacts. In the early stages, they're often stored directly on the server disk—this seems like the simplest and fastest solution. But as the application grows, this approach begins to noticeably increase costs and complicate infrastructure operations. Why It's Unprofitable to Store Files on Server Disks The main disadvantage is tying data to a specific machine. If a server needs to be replaced, expanded, or moved, files have to be copied manually. Scaling also becomes a problem: the more data stored, the faster disk costs grow, which are always more expensive than cloud storage. Another complexity is fault tolerance. If something happens to the server, files are at risk. To avoid this, you have to configure disk duplication or external backups—and that's additional costs and time. How Object Storage Reduces Costs S3 object storage removes most of these limitations. Data is stored not on a specific server, but in a distributed system where each file becomes a separate object with a unique key. Such storage is cheaper, more reliable, and doesn't depend on specific applications or VMs. The economic effect is immediately noticeable: Volume can be increased without migrations and downtime Files are automatically distributed across nodes, ensuring fault tolerance No need to pay for disk resources of individual servers Easier to plan the budget—storage cost is predictable and doesn't depend on machine configuration Where to Use S3 in Applications S3 is convenient to use where data should be accessible from multiple parts of the system or where scaling is important: Images and user content Web application static files Archives and exported data Backups CI/CD artifacts Machine logs that then undergo processing This separation reduces the load on application servers and gives infrastructure more flexibility. S3 Features in Hostman In Hostman, object storage integrates with the rest of the platform infrastructure and works on the S3-compatible API model, which simplifies the transition from other solutions. Lifecycle policies are also supported: you can automatically delete old objects, move them to cheaper storage classes, or limit the lifespan of temporary files. This helps optimize costs without manual intervention. Integration with virtual servers and Kubernetes services makes S3 a convenient architecture element: the application can scale freely, and data remains centralized and reliably stored. Containerization: How to Ensure Stability and Reduce Operating Costs Containerization has become a basic tool for projects where it's important to quickly deploy environments, predictably update services, and flexibly work with load. In addition to development convenience, it also provides tangible savings: a properly configured container architecture allows using infrastructure much more efficiently than the classic "one server—one application" model. Why Containers Are Cheaper to Operate Unlike virtual machines, containers start faster, take up fewer resources, and allow placing multiple services on the same node without risks to stability. The team stops maintaining multiple separate servers "for every little thing"—all services are packaged in containers and distributed across nodes so that resources are used as densely as possible. This reduces infrastructure costs and decreases the number of idle machines. Savings Through Kubernetes Kubernetes has a particularly noticeable impact on the budget. It automatically adjusts the number of containers to the load: if traffic has grown, it spins up new instances; if it has fallen, it stops excess ones. The project pays only for actual resource usage, not for reserves maintained for peak values. In addition, Kubernetes simplifies fault tolerance. Applications are distributed among different servers, and the failure of one node doesn't lead to downtime. This reduces costs associated with failures and decreases the need for expensive backup servers. Less Manual Work, Lower Costs In container architecture, updates, rollbacks, test environment deployments, and scaling turn into automated processes. The team spends less time on administration, which means less money on operational tasks. Kubernetes also allows running environments for the duration of tasks. For example, spinning up environments for CI/CD, load testing, or preview—and automatically deleting them after work is completed. Kubernetes in Hostman Kubernetes is provided as a fully managed service (KaaS). The platform handles updating master nodes, network configuration, fault tolerance, and the overall state of the cluster. The team works only with nodes and containers, avoiding routine DevOps tasks. Nodes can be added or removed literally in minutes. This is convenient when the load fluctuates: infrastructure quickly expands or contracts, and the budget remains predictable. Integration with object storage, network services, and managed databases makes Kubernetes part of a unified architecture where each element scales independently and without unnecessary costs. Network and Security Without Unnecessary Costs When designing network architecture, it's easy to make mistakes that not only reduce system resilience but also increase the budget. How Improper Network Organization Increases Budget Even small flaws in network configuration can cause a noticeable financial drain. For example, if an internal service is accessible via a public IP, traffic starts passing through an external channel, which increases latency and data transfer costs. A similar situation arises when the database and backend are on different servers but not connected by a private network. Some cloud providers might meter such traffic, which can become an unexpected expense. In Hostman, data transfers are free, but a private network still offers advantages: higher transfer speeds, reduced security risks, and the ability to avoid unnecessary public IPs. Without private networks, security also becomes more complicated. To restrict access, you have to build additional firewall rules and load balancers, and each such solution costs money, be it in the form of resources or human hours. Savings Start With Network Structure In a rational network organization, each component operates in its proper zone and routes traffic to where it's safe and free. Private networks allow isolating sensitive services (databases, internal APIs, queues) and completely removing them from public space. This reduces the attack surface, decreases the number of required firewall rules, and eliminates costs for unnecessary traffic. Floating IPs help save on fault tolerance: instead of reserving a powerful server, it's enough to prepare for quickly transferring the address to another VM. Switching happens almost instantly, and the service remains available for users. This scheme allows ensuring resilience without the expense of duplicate configurations. Reducing Costs Through Fault Tolerance Improperly configured networks often cause downtime, and downtime means direct losses. Proper load distribution, load balancers, and private routes allow avoiding a situation where one server becomes a bottleneck and takes the application out of service. A separate point is DDoS protection. This is not only about security but also about economics: during an attack, the service can become unavailable, and unavailability almost always means losing customers, orders, and reputation. DDoS protection cuts off malicious traffic before it enters the infrastructure, reducing server load and preventing downtime that easily turns into tangible losses. Automation: How to Reduce Operating Costs Even perfectly selected infrastructure can remain expensive if managed manually. Creating test environments, updating configurations, scaling, backup rotation, server management—all this turns into a long chain of manual actions that take hours of work and lead to errors. Automation reduces maintenance costs through repeatability, predictability, and the elimination of human error. Why Manual Infrastructure Is More Expensive Manual operations always mean: Risk of forgetting to delete a temporary environment Inconsistent settings between servers Unpredictable downtime due to errors Developer time spent on routine instead of the product These are direct and indirect costs that easily hide in the process but noticeably increase the final budget. Which Processes Are Most Profitable to Automate From a savings perspective, three areas provide the most benefit: Environment Deployment. Quick creation of environments for development, testing, preview, and load tests. The environment is spun up automatically, works for the required time, and is deleted when no longer needed. Infrastructure Scaling. Load peaks can be handled automatically: spin up additional resources based on metrics, then shut them down. This way, you pay only for the peak, not for maintaining a constant reserve. Unified Configuration Description. When the environment is described as code, it can be reproduced at any stage, from development to production. This reduces the number of errors and eliminates "manual magic." Infrastructure as Code: An Economic Tool IaC solves the main problem of the manual approach: unpredictability. Configuration is stored in Git, changes are tracked, environments are created identically. The team spends less time on maintenance, plans the budget more easily, and responds to load changes faster. As a result, operating costs are reduced, and infrastructure becomes more transparent and manageable. Hostman Tools for Automation Hostman provides a set of tools that help build automation around the entire infrastructure: Public API. Automatic management of servers, networks, databases, and storage. Terraform provider, for a complete IaC approach: the entire infrastructure is described as code. cloud-init. Allows deploying servers immediately with preconfigured settings, users, and packages. Together, they create infrastructure that can be spun up, modified, and scaled automatically, without unnecessary actions and costs. This is especially important for teams that need to move quickly but without constant overspending. Conclusion Optimizing infrastructure costs is about building a mature approach to working with resources. At each stage, it seems that costs are quite justified, but in total they turn into a tangible burden on the budget—especially if the team scales quickly. To keep spending under control, it's important not to cut resources blindly, but to understand how infrastructure works and which elements the product really needs here and now. An audit helps find inefficient parts of the system. Correct work with computing power and databases reduces costs without loss of performance. Transition to object storage makes the architecture more flexible and reliable. Containerization and Kubernetes remove dependence on manual actions. Automation frees the team from routine and prevents errors that cost money. Proper network organization increases resilience—and simultaneously reduces costs. Rational architecture is not about saving for saving's sake. It's about resilience, speed, and the project's ability to grow without unnecessary technical and financial barriers. And the earlier the team transitions from chaotic resource accumulation to a thoughtful management model, the easier it will be to scale the product and budget together.

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.