How to manage and optimize Spark's unified analytics engine for both batch and streaming data?

Managing and optimizing Spark's unified analytics engine, which can handle both batch and streaming data, means ensuring your Spark jobs run efficiently and effectively. Here's a simple guide to get you started:

1. Choose the right cluster manager: Apache Spark can run on various cluster managers including YARN, Mesos, Kubernetes, or its own standalone cluster manager. Select one that aligns well with your existing infrastructure and scalability needs.

2. Use DataFrames and Datasets: When processing data, opt for DataFrames and Datasets instead of the lower-level RDD API for better optimization through Spark's Catalyst optimizer.

3. Monitor your Spark jobs: Keep an eye on the Spark UI. It provides information on the execution of tasks, memory consumption and helps in debugging any performance issues.

4. Partition your data wisely: Ensure your data is partitioned effectively across the nodes in your cluster. This reduces shuffling of data (movement across nodes) which is a costly operation in terms of time and network I/O.

5. Optimize shuffles: If you can't avoid data shuffling, try to minimize it by using operations that reduce shuffle overhead, like reduceByKey instead of groupByKey.

6. Cache judiciously: Persist data in memory when you need to access it multiple times. Use the storage levels wisely, for example, MEMORY_AND_DISK when you run out of memory.

7. Tune resource allocation: Configure the amount of memory and cores for Spark executors adequately. Don't allocate too much (might underutilize resources) or too little (might slow down processing).

8. Manage memory usage: Understand how Spark uses memory and possibly adjust memory fraction settings like spark.memory.fraction and spark.memory.storageFraction to optimize.

9. Optimize for data locality: Arrange for your data to be as close as possible to the computing resources. This means that tasks are executed where data is located, reducing data transfer time.

10. Use broadcast variables and accumulators: For large, read-only lookup tables, broadcast variables can be useful in distributing the data to all workers. Accumulators can be used for counters or sums efficiently.

11. Check serialization: Make sure you're using efficient serialization—Kryo serialization can be faster and more compact than Java serialization.

12. Deal with skewed data: Identify and handle skewed data in your Spark jobs. Techniques like salting can help to distribute the skewed keys more evenly.

13. Optimize streaming jobs: For streaming data, leverage the structured streaming API for higher-level abstractions and better optimizations.

14. Upgrade Spark versions: Newer versions of Spark come with various performance improvements and bug fixes, so keep your Spark versions updated.

15. Analyze and iterate: After tuning, analyze the performance with metrics and logs. It's a continual process of iteration to fine-tune settings for your specific workloads.

Remember, optimization is often specific to the data and the job at hand. There's no one-size-fits-all approach, so experimenting and benchmarking with different configurations is key to finding what works best in your context.