Big data systems integrate with analytics platforms through three primary stages: data ingestion, processing, and analysis. First, data is collected from diverse sources (databases, logs, sensors) and ingested into storage systems like Hadoop Distributed File System (HDFS), cloud storage (Amazon S3), or data lakes. Tools like Apache Kafka or Apache NiFi handle real-time streaming or batch data transfers. Next, processing frameworks such as Apache Spark or Flink transform and clean the data, preparing it for analysis. Finally, analytics platforms (Tableau, Power BI, or custom Python/R scripts) connect to processed data via APIs or query engines (Apache Hive, Presto) to generate reports, dashboards, or machine learning models. This pipeline ensures raw data becomes actionable insights.
For example, a retail company might use Kafka to stream sales transactions into a cloud data warehouse like Snowflake. Spark jobs could aggregate daily sales by region, storing results in Parquet files optimized for querying. Analysts then use SQL-based tools like Looker to visualize trends. In another scenario, a IoT platform might ingest sensor data via AWS Kinesis, process it with AWS Lambda for real-time anomaly detection, and feed results into Grafana dashboards. These integrations often rely on connectors (JDBC/ODBC drivers) or intermediate layers like REST APIs to bridge storage systems (e.g., Hadoop) and analytics tools. Cloud providers simplify this with managed services—Google BigQuery directly integrates with Data Studio, while Azure Synapse links to Power BI.
Challenges include managing data latency (real-time vs. batch), schema consistency, and scalability. For instance, a dashboard pulling from a Hadoop cluster might face delays if Spark jobs aren’t optimized. Developers often address this by partitioning data or using columnar formats (Parquet) for faster queries. Security is another concern: access controls must align between systems (e.g., AWS IAM roles governing S3 and Redshift). Best practices involve standardizing data formats, using metadata catalogs (AWS Glue), and automating pipelines with tools like Airflow. Proper integration ensures analytics platforms can query the most recent, clean data without overloading the big data infrastructure.