20 Beste Big Data Customer Analytics Software Für 2026

Co-founded by the creators of Apache Kafka, Confluent is popular among data engineers and developers who require a scalable platform for working with data in motion.

Why I picked Confluent: One of the cool things about Confluent is that it provides a unified platform for data in motion. It integrates data from various sources, such as databases, applications, and IoT devices. This enables users to gain immediate insights from streaming data.

Confluent’s ability to provide real-time monitoring of data, intuitive UI, data visualization capabilities, and security and compliance features make it an excellent tool for data and business analysts. Also, it uses Apache Kafka as its core technology to provide a scalable and low-latency platform for handling high volumes of streaming data.

Confluent Standout Features and Integrations

Features include real-time analytics, data querying, Hadoop integration, Spark integration, multi-source analysis, data lake, data visualization, data workflow, governed discovery, data processing, data sources, integration, real-time processing, reporting, and analytics.

Integrations include Ably, Adobe experience platform, Aerospike, Amazon S3, Azure Cosmos DB, Azure Data Explorer, Couchbase, Crux, DataStax, Datadog, and other software options.

Starburst helps enterprises query big data and acts as a single source of truth for your data.

Why I picked Starburst: Traditional data preparation and ETL processes are time-consuming and slow. Starburst enables ad-hoc analytics without the need for ETL processes. It quickly processes data across multiple sources and delivers results within seconds. This way, organizations can speed up time-to-market and improve decision-making.

Moreover, Starburst offers features like fine-grained access control, data masking, and end-to-end data encryption to ensure data security. In addition, it integrates with authentication providers like LDAP or Kerberos, which ensures authentication and provides administrators control access to resources. Starburst also offers a wide range of connectors so organizations can easily access data from various sources.

Starburst Standout Features and Integrations

Features include SQL query engine, distributed architecture, security and data governance, cloud-native deployment options, multi-cluster management, and consulting and training services.

Integrations include Accumulo, Google Big Query, Cassandra, Clickhouse, Delta Lake, Druid, Amazon DynamoDB, Elasticsearch, Greenplum, JDBC, and other software options.

Countly is a product analytics platform that helps you understand how users interact with your mobile, web, and desktop applications. It provides real-time insights into user behavior, enabling you to make informed decisions to enhance your products. 

Why I picked Countly: It offers an extensive feature set tailored for in-depth analysis. Features like user profiles and cohorts allow you to segment your audience effectively, helping you identify patterns and trends within different user groups. 

I also like Countly's real-time data collection and visualization capabilities. You can monitor user actions as they happen, which is crucial for timely decision-making. This immediacy allows you to respond quickly to user behavior, optimizing the user experience and addressing issues promptly.

Countly Standout Features and Integrations

Features include retention analysis, funnels, heatmaps, push notifications, surveys, times-of-day analysis, remote configuration, compliance hub view, reCAPTCHA security, user analytics, and dashboards.​

Integrations include Facebook Leads, Google Sheets, TiktokLeadGeneration, Gmail, Facebook Offline Conversions, Mailchimp, Slack, Shopify, Trello, Telegram, Discord, and WooCommerce.

The flagship program of MathWorks, MATLAB provides a programming environment to engineers, scientists, and data analysts.

Why I picked MATLAB: The best thing about MATLAB is its ability to process and visualize large data sets. In addition, its built-in graphics tool provides various options for visualizing data in two and three-dimensions.

MATLAB has become increasingly popular in recent years due to its large library of tools and functions for numerical computation and data analysis. In particular, it offers specialized toolboxes for data analysts and machine learning engineers. These toolboxes can help you build and evaluate machine learning models, data preprocessing, feature selection, and extraction.

MATLAB Standout Features and Integrations

Features include 3D/solid modeling tools, editing tools, drawing tools, rendering tools, finite element analysis, multibody dynamics, discrete event modeling, continuous event modeling, mechanical event simulation, manufacturing process simulation, hardware support, and command clarity.

Integrations include Simulink, Python, Microsoft Excel, LabVIEW, C++, Stateflow, Oracle, Mathcad, SolidWorks, ANSYS, and other software options.

Dash Enterprise helps data scientists, analysts, and developers create custom dashboards and applications without spending hours on coding or complicated infrastructure.

Why I picked Dash Enterprise: The feature that sets Dash Enterprise apart from other dashboard platforms is that it integrates machine learning models into dashboards. Hence, users can analyze and visualize data in real-time, identify patterns, and make predictions. 

In addition, it allows users to collaborate on dashboards and applications with people in their organization or external stakeholders. It provides enterprise-level security features such as role-based access control, SSL encryption, and LDAP integration. Also, it can handle large datasets, making it ideal for enterprise-level use cases.

Dash Enterprise Standout Features and Integrations

Features include data app workspaces, design kit, AI app catalog, report engine, dashboard engine, app manager, job queue, embedding, filesystem, database integration, CI/CD, authentication middleware, and Databricks integration.

Integrations include Databricks, MySQL, Postgres, Snowflake, Google BigQuery, Amazon RedShift, MS Azure SQL, Redis, MongoDB, Elasticsearch, Salesforce, and other software options.

Databricks Lakehouse Platform provides a Lakehouse architecture to companies that want to unify data warehousing and analytics on a single platform.

Why I picked Databricks Lakehouse Platform: It integrates with Delta Lake, an open-source data format that provides ACID transactions, schema enforcement, and other performance optimizations for cloud storage. Delta Lake allows organizations to handle complex workloads and ensure data consistency, reliability, and accuracy, making it an essential component of the Lakehouse architecture.

It is ideal for enterprises that need to process and analyze large volumes of data from various sources in a scalable, cost-effective, and secure manner. It is especially useful for data engineering and data science teams who want to collaborate on data projects, as it provides a unified platform for building, managing, and deploying data pipelines. Also, it can handle batch and streaming data processing using the Delta Lake format, ensuring data consistency, reliability, and accuracy.

Databricks Lakehouse Platform Standout Features and Integrations

Features include real-time data collection, data lake, data distribution, Hadoop integration, Spark integration, machine scaling, data preparation, cloud processing, workload processing, data integration, data compression, data lake integration, and scalability.

Integrations include Fivetran, arcion, Rivery, dbt, Prophecy, Tableau, Microsoft Power BI, Hex, Salesforce, Google Analytics, and other software options.

dbt (Data Build Tool) is a popular data transformation tool that helps data analysts and engineers perform the ‘transform’ step in Extract-Transform-Load (ETL) pipeline.

Why I picked dbt: When working on a complex and large data pipeline, team members usually simultaneously work on different aspects of the data pipeline, which runs the risk of conflicting changes. dbt’s version control feature eliminates this risk since it helps users keep a history of changes, and you can roll back to previous versions if needed.

This tool is for data professionals and organizations who struggle with data transformation and storage. Incorporating it into your workflow will help you reduce your workload. Additionally, the built-in testing feature ensures that the transformations are accurate.

dbt Standout Features and Integrations

Features include browser-based IDE, job scheduling, unlimited daily runs, logging and alerting, data documentation, source freshness reporting, continuous integration, API access, semantic layer, Single Sign On (SSO), multiple deployment regions, Service Level Agreements (SLAs), role-based ACLs, fine-grained Git permissions, audit logging, and native support for GitHub, GitLab, and Azure DevOps.

Integrations include AlloyDB, Amazon Redshift, BigQuery, Databricks, Snowflake, Deepnote, Hex, Houseware, Lightdash, Mode, and other software options.

Dremio is a data-as-a-service (DAAS) platform that provides self-service analytics tools to data analysts, engineers, and business owners.

Why I picked Dremio: The best feature of Dremio is its ability to speed up query performance through Reflections. These are pre-aggregated and indexed data structures that make it easier and faster to analyze data. In addition, Dremio automatically refreshes reflections so that query results are updated. 

In addition, Dremio can help you modernize current systems, migrate legacy infrastructure to the cloud, update existing data lakehouse, and build new projects. It also offers a simple interface and good documentation, making it easy to troubleshoot any issues.

Dremio Standout Features and Integrations

Features include a lakehouse platform, global control plane, infinite scale and concurrency, self-service data curation and sharing, built-in governance and lineage, transparent query acceleration, SQL DML on the lakehouse, BI tool integration, audit logs, social identity provider integration, SOC 2 Type 2, ISO 27001, HIPAA Compliant, community support, enterprise identity provider integration, data masking, and SCIM synchronization.

Integrations include Airbyte, Alteryx, AWS, Collibra, dbt Labs, GoodData, intel, IBM, Microsoft, Okera, and other software options.

Tinybird is a data analytics platform for developers who want to build real-time data products at scale using SQL-based API.

Why I picked Tinybird: The best aspect of Tinybird is its architecture, which enables quick real-time data processing and analysis. The platform's in-memory data storage and pre-built connectors to popular data sources enable lightning-fast query performance. It uses SQL-based query language, which means developers can use their existing skills to analyze data.

Tinybird is an ideal solution for organizations seeking a robust and scalable data processing platform. It is especially well-suited for use cases that require quick response times and low latency, such as real-time analytics, monitoring, and alerting.

Tinybird Standout Features and Integrations

Features include unlimited seats, unlimited data sources, data connectors, multi-region support, unlimited API endpoints, realtime materialization, secured data, and technical support.

Integrations include Node, Python, Schedule, Data Stores, Airtable, GitHub, Filter, Bash, Twilio, Discord, MySQL, AWS, and other software options.

Exasol is an in-memory database system ideal for organizations in data-intensive industries, such as finance, healthcare, and e-commerce.

Why I picked Exasol: Exasol in-memory technology allows data storage in the computer’s memory rather than on disk, allowing for rapid real-time access and analysis of data. Also, it is based on shared-nothing architecture, which distributes the workload across multiple nodes or computers, allowing for parallel processing.

Another noteworthy aspect of Exasol is its machine learning capabilities that enable users to perform advanced data analysis and prediction tasks. Users can build, train, and deploy machine learning models directly within the database system. This allows for faster and more efficient data analysis, as there is no need to transfer data to external ML tools for analysis.

Exasol Standout Features and Integrations

Features include distributed database, standard SQL, distribution keys, commodity hardware, profiling, IMPORT Command, EXPORT Command, XML/RPC interface, documentation, virtual schemas, ACID transactions, system tables, auditing, session management, user management, Lua scripts, preprocessor scripts, UDF scripts, machine learning, skyline queries, geospatial data, grouping sets, and connectors.

Integrations include Amazon QuickSight, Business Objects, IBM Cognos, Looker, Metabase, pentaho, Amazon Kinesis, Keboola, Kafka, Python, SQL, and other software options.