How can we handle data that exceeds memory size, having tried previous solutions?

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There are a few strategies that can be employed to handle data that exceeds memory size:

1. Use external storage: One approach is to use an external storage system, such as hard drives or network-attached storage (NAS), for storing the data. The data can be partitioned or divided into smaller, manageable chunks that can be stored across multiple drives.

2. Employ distributed computing: Another solution is to employ distributed computing, which involves dividing the data and processing across multiple computers or servers. Distributed computing systems such as Hadoop or Apache Spark can be used to manage and process the data in a distributed manner.

3. Utilize cloud computing: Cloud computing provides access to large-scale, distributed computing infrastructure for processing large amounts of data. The data can be stored in a cloud storage service like Amazon S3 or Microsoft Azure, and processed using cloud-based data processing services like Amazon EMR or Microsoft Azure HDInsight.

4. Use compression and serialization techniques: Data can be compressed and serialized into a smaller size, making it easier to manage and process. This can also help reduce storage and processing costs.

5. Consider database management systems: A relational database management system (RDBMS) or a NoSQL database can be used to store and manage the data. These systems provide efficient retrieval and querying of large datasets.