Here's how you can design a data architecture for real-time analytics.

To determine the optimal technology stack , I recommend first conducting a thorough analysis of the data ingestion volume (per minute/second), the expected user base, and a robust forecast of data growth over the next 5-10 years. Armed with this information, you can make an informed decision about whether Kafka or a custom-built model best balances cost-effectiveness and scalability.

A ingestão de dados é a base de qualquer plataforma, quando vamos selecionar as ferramentas devemos levar em consideração o tipo da origem e dados, sempre que possível utilize abstrações que funcione com parâmetros permitido a escala. Quando falamos em eficiência e velocidade de ingestão é fundamental utilizar cargas incremetais e origens modeladas no conceito de eventos. Idealmente trabalhar no modelo ELT somente insert(append-only), agilizando a disponibilidade dos dados no lake. Para latência mínima utilize a arquitetura de stream de eventos, plataformas como o kafka facilitam a implementação dessa arquitetura.

1.Data sources to be understood to check if it is capable to handle extra load of new readers. 2. How time series data will be accumulated at transit to handle data loss due communication issues.

Establish Reliable Data Ingestion: Select tools like Apache Kafka or AWS Kinesis for collecting and moving data efficiently from various sources. Ensure Minimal Latency: Design the ingestion process to handle data with minimal delay, akin to a high-speed rail system, ensuring timely data availability. Efficient Data Transport: Implement systems that can transport large volumes of data swiftly, maintaining the integrity and speed of data transfer. Quick Data Analysis: By minimizing latency in data ingestion, enable faster analysis and decision-making processes. Scalable and Robust Architecture: Create a scalable and robust architecture that can handle varying data loads and ensure continuous, real-time data processing.

Unlike batch processing, stream processing handles data in real-time as it arrives. This can be likened to a real-time quality control system on a production line, where each data point is immediately inspected and processed. This method is essential for applications that rely on up-to-the-minute data, as it ensures that information is always current and actionable. Tools like Apache Kafka and Amazon Kinesis are often utilized for stream processing, enabling continuous data flow and immediate analysis. By implementing real-time processing, organizations can swiftly react to new data, gaining timely insights and making informed decisions without delay.

Apache Flink is replacing Kafka streams in my opinion. It is easier to work with for processing, transformation, and enrichment. Check out quix.io or quix streams as well for tapping into Kafka with python.

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Stream Processing Engine is the heart of Realtime architecture. So it matters how efficiently it circulates data & meta - Streamlining Business Process will be the key to change mindset. - Change cannot be achieved by constantly challenging legacy status quo’s. - Architect to Distribute workload and encourage parallel processing - Decompose complex tasks into modular lean tasks. - Resolve dependency and conflicts. - Avoid bottlenecks and switching gears to file or batch impacting latency. - Maintain updated metadata and sample payload repository throughout the architecture. - Goal is to achieve more in less amount of time. Utilize benefits of Kaizen Process Management techniques. - The meaning of Kai is (change) and Zen is (for the better).

The key to real-time/near real-time/ streaming systems is the ability to capture the incoming data stream without losing the data. Scalability and the fan-in aspects of the ingestion funnel are critical. Also, planning to sink the incoming streams into a persistence layer without applying any filters and logic at the ingestion gate is very much necessary, without worrying about the structure of the incoming data. Systems such as object storage/ sink equivalents such as Dynamo DB/ redshift would be a good fit.

Real Time analytics and data streaming requires high scalability and high performance. NoSQL DB works best when the data is unstructured. Another thing that companies are facing is the cost of transactions on the cloud platforms. This necessitates to pick the right tool based on the volume and processing of the data. Mongo DB, snowflake are few of the emerging systems for data processing and analytics

Choosing the right database depends on the type of application usage, whether it is a finance report based on current market trends or a media streaming application or a trading platform, along with the usage choosing the right database needs analysis of the key factors such as: high scalability, low latency, ability to support complex aggregations, support real time data ingestion, and high performance to support sub second queries on huge volume of data while supporting concurrent queries.

Choosing the rate data storage for your firehouse of data off Kafka is crucial. Top choices are InfluxDB, QuestDB, Citus, TimescaleDB, Cassandra. Your goal is to find a database that is capable of handle extremely high write rates and is optimized for time series data if necessary (such as IoT sensor data with a timestamp). High availability, clustering option, table sharding distribution, SQL support are other requirements you may have.

Além disso, vale destacar as ferramentas de visualização que tem maior integração com seu ambiente analítico. Algumas ferramentas terão integração até com o metadado das informações, que irá criar o Dashboard como ativo na organização e trazendo toda linhagem dos dados utilizados para construção do Dashboard. O banco de dados escalável, gerenciado por uma nuvem, com possibilidade de usar processamento em memória, que o refresh do Dashboard seja eficiente nas integrações on-line com o banco será o sucesso do seu projeto.

In-memory or no-SQL databases are good to store the latest streaming data, maybe running some analytics in memory. However, if large amounts of data and historical data are needed for analysis, the latest streaming data may have to be copied into large analytics databases or data lakes. Also cloud data lakes and analytics databases integrate with streaming services well.

For real-time analytics, use a stream processing framework like Kafka for data ingestion. Store processed data in a scalable cloud-based data warehouse such as Amazon Redshift or Google BigQuery. Deploy in-memory databases for low-latency access. Leverage cloud-native services that handle tasks ranging from predictive analytics to natural language processing and image recognition. The end result is a robust architecture that enables insightful, real-time data analytics.

One thing I’ve found helpful in real-time analytics is choosing tools that excel in processing data instantly and integrating seamlessly with existing systems. Complex event processing (CEP) engines and real-time BI platforms are particularly effective due to their speed and scalability. These tools provide on-demand analytical capabilities, enabling proactive decision-making and significantly enhancing operational efficiency.

You may want tools that are lightning-fast, but my experience with BI tools is not that straightforward and usually provides insight in milliseconds doesn't happen. As always, there is more to it, but the models under the BI tools are generally quite complex/visualised and, in the grand scheme, structured and dont have much data. There are tools that can do real-time work, but most are bespoke views created by experts, not an average user. Analytical tools are improving and claim to be able to achieve faster real-time analytics, and with AI capabilities, they certainly are achieving more than five years ago. The power is usually in the data architecture underneath, not the tool that presents the results.

Complementando. Quase todas as ferramentas atuais estão preparadas para dados real-time. O que vai diferenciar na sua escolha são as tecnologias envolvidas na sua arquitetura de dados. Quase todas essas ferramentas rodam em todas nuvens e banco de dados, porém algumas terão diferenciais de conexões mais eficientes no databricks, outro no bigquery, outro no redshift. Facilitando desde a leitura do metadado dos dados até na gestão de ativos no market place de dados. Algumas necessidades é na exposição dos dashboards em SDK e embedded, organização dos dados fazendo push down de processamento escalável nos serviços em nuvens. Vale esta análise também para garantir dados real-time.

Using better analytics tools to consume the stored data with no latency highly recommended. Tool should be capable enough to handle real time transformation and conversations on the fly, there are some tools in the market to handle realtime analytics with better performance, tools are like Tableau, Power BI, Looker etc.

Less is more in Data Visualization. It's not recommended to cramp to many data elements in the same visualization. It's best to adopt simple and impactful and consistently experience to user. The color palette should be in light color align to secondary branding color. Data source should be transformed and normalized in Data Lake to avoid lag in data visualization. Power BI and Tableau are good choice as data visualization tool.

Vale destacar um ponto aqui em visualização de dados em tempo real. Quando necessário e irá gerar valor para o negócio, sensacional. Viável de implementar porém valor mais alto para manter. Estude a melhor forma, não use tabela histórica, otimiza query, utilize estrutura de streaming se possível, não misture com outros relatórios batch, pois a ferramenta de visualização também tem seu tempo de atualização a ser configurado.

Data visualization for real-time analytics translates data streams into visual formats that are easy to understand and act upon instantly. Effective data visualization for real-time analytics is crucial for translating continuous data streams into actionable insights. By leveraging real-time dashboards, interactive visualizations, geospatial mapping, and customizable reports, organizations can provide decision-makers with instant, clear, and actionable information. Ensuring scalability, performance, interactivity, and security are essential for a robust real-time visualization solution.

Ensure data availability for visualization by using redundant data sources and robust streaming platforms like Apache Kafka. Implement stream processing with Apache Flink to handle data on the fly, ensuring minimal downtime. Store the data in a highly available, low-latency database such as Apache Druid, which supports quick querying. Visualization tools like Power BI, Qlik, or Grafana can connect directly to your real-time data sources and offer interactive dashboards. These tools allow for real-time updates, ensuring decision-makers have access to the most current data for timely insights.

Like a dashboard in a race car, consider how much time is available to read it (a few seconds on the straights). Display the fewest data points with maximum meaning on limited real estate. Don't add data that delivers no value. That is why speedometers are absent because speed is a result of good driving viewed at the end, not in real time. More important is data a driver can control (gear position, rpm).

When doing this we need to think about a defense in depth approach with multiple layers of security. Starting with modern access controls such as managed identities, where the application is authorised using certificate based authentication and restricted to certian IP ranges or servers. Next level of securing access to your sensative content is to ensure that access is limited to certain times or better still provided based on an out of band request (just in time provisioning) Finally you must ensure that only the required data is visible to your application and in turn user with data encrypted at every stage.

The heart of real-time analytics lies in the analytical tools you choose. These tools must be capable of handling continuous streams of data and providing insights in milliseconds. You'll be required to opt for solutions that are specifically built for speed and real-time computation etc.

A detailed security architecture is critical in real-time analytics to protect sensitive data from breaches and ensure compliance. It employs robust authentication, monitors for threats in vast data streams, and uses AI for anomaly detection. This proactive defense adapts to evolving cyber threats, maintaining the integrity and confidentiality of real-time processed data, which is essential for trustworthy and actionable insights. Most important, given the analytics is real-time a security breach can end up costing a significant loss

Always Remember, the specific design of your real-time analytics architecture will depend on your organization's unique requirements, data sources, and use cases. It's essential to start with a well-defined strategy and choose the right tools and technologies to achieve your real-time analytics goals.

In the majority of modern use cases, "real time analytics" is a misleading description. We must define the precise constraints involved in data management and the requirements related to "real time" availability. Data requires ingestion, processing, cleansing, and quality control before it is useful for insights and analytics. Each of these processes take time. Analytics & visualization data management can also take time. We must optimize our analytic processes without losing data quality or key insights. Will you make compromises in the ingestion process or (as is too often the case) in quality control? We must clearly define what it is meant by "real time" and how we will manage the data and analytics processes to support it.

Data validation and quality control are crucial in streaming data analytics due to the continuous and rapid flow of data. Ensuring data integrity in real-time applications, like IoT monitoring, is essential. A specific approach involves implementing validation rules directly in the data pipeline. This includes schema validation starting from the very first ingestion steps to ensure data format consistency, range checks to catch outliers, and completeness checks to verify no data loss. Additionally, anomaly detection algorithms can identify and flag unusual patterns. Automated alert systems then notify teams of potential issues. Data Quality in streaming architecturzs is a key towards reliability and maximum value for the buisness.

Also consider: - Data cataloging so it's easy to satisfy on demand analytics. Not knowing what data you have will make it unusable. - Data grading so data is used for only its intended purpose.

Be sure that you have the right team with the right skills and experience. Also be sure that your business analysts, product owners, product managers, and project managers understand that data platforms are special creatures that need specific care and attention paid to them. I have seen many talented software management professionals fall flat on their faces when dealing with data platform projects. You NEED to set these projects up for success and you need to start with the right people.