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Spark's capabilities extend far beyond basic data processing, thanks to its rich **ecosystem**. **Spark SQL** allows you to query structured data using SQL-like syntax, making it easy to analyze data in formats like **Parquet**, **JSON**, and **CSV**. For real-time processing, **Spark Streaming** enables you to ingest and process data streams from sources like Kafka and Flume. Spark also includes powerful libraries for machine learning (**Spark MLlib**), graph processing (**GraphX**), and more. Knowing about the **Spark ecosystem** can help you determine the best tools for the job. Spark is written primarily in **Scala**, but it offers APIs in **Python** (**PySpark**), Java, and R, providing flexibility for developers with different language preferences. PySpark is particularly popular due to Python's versatility in the data science and data engineering communities.
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