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This search is based on elasticsearch and can look through several thousand pages in miliseconds.
Learn more
This search is based on elasticsearch and can look through several thousand pages in miliseconds.
Learn moreIn the BI field, we have been relying on the ELK Stack for some time now. It is a cost-effective yet powerful way to analyze large volumes of data in real time. We offer technical implementation and would be happy to provide the appropriate infrastructure for your company.
Implementing Business Intelligence (BI) with the ELK Stack (Elasticsearch, Logstash, Kibana) is a cost-effective and powerful way to analyze and visualize large amounts of data in real time. The ELK Stack is particularly well-suited for BI applications that require fast querying, real-time monitoring, and interactive visualizations.
With the ELK Stack, you can build a cost-effective, flexible BI platform that enables real-time analytics, user-friendly dashboards, and data-driven decision support. Using Logstash and Beats for data ingestion, Elasticsearch for storage and analysis, and Kibana for visualization provides a powerful solution for BI needs.
In addition to traditional consulting for your ELK stack and how to use it in BI, we primarily offer our technical expertise. With our DevOps and infrastructure team, we’re able to set up and operate the stack for you. Our focus is on technically sound implementation.
Logstash: Logstash is an ETL (Extract, Transform, Load) tool that can import data from various sources (databases, CSV files, APIs, logs, etc.) into Elasticsearch. For BI applications, you can configure Logstash to process data in real time and transform it as needed. For example, you can perform currency conversions, aggregations, or calculations directly during data ingestion.
Beats: Beats is a collection of lightweight data collectors that send data to Elasticsearch or Logstash. Filebeat and Metricbeat are popular tools for collecting logs and metrics from servers and applications and storing them in Elasticsearch. For BI applications, Filebeat can be used to collect logs, while Metricbeat provides performance metrics from systems and applications.
Indexing: Elasticsearch stores the imported data as indexes. These indexes are similar to tables in relational databases and contain the BI data needed for analysis. The advantage of Elasticsearch is that it can efficiently handle even large volumes of data and offers fast search speeds.
Aggregation and Analysis: Elasticsearch features a powerful query language that supports both simple search queries and complex aggregations. In BI applications, this allows metrics such as averages, totals, and groupings to be calculated directly on the data in Elasticsearch to gain quick insights.
Creating Dashboards: Kibana is the visualization tool in the ELK Stack and is ideal for BI dashboards. You can create dashboards that visualize data and display KPIs such as sales figures, user activity, or system metrics in real time.
Filtering and Drilldowns: Kibana enables interactive dashboards that allow users to gain detailed insights into data through filters and drilldowns. For example, you can filter data by date, region, or product group and dynamically display the results.
Reporting and Export: Kibana also offers the ability to create reports and share them with stakeholders. These reports can be generated manually or automatically and contain aggregated BI information and visualizations.
Real-time Analysis: The ELK Stack enables real-time data analysis, ensuring that BI dashboards always display the most up-to-date data. This is particularly useful for companies that need to make decisions based on live data, such as sales figures or user interactions.
Alerts and Notifications: In combination with Elastic Alerting, you can configure alerts that respond to specific conditions. For example, alerts could be triggered when sales figures reach a certain level or metrics show unusual values. This allows business decisions to be made more quickly and based on data.
Scalability: Because Elasticsearch and the ELK Stack are horizontally scalable, BI applications can be run efficiently even as data volumes grow.
Performance Optimization: Through sharding and replication, you can further increase the availability and speed of BI applications, ensuring that BI users can rely on fast queries even when dealing with large amounts of data.
