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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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Social Analytics - How Marketers Will Use

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Of all the windows through which a business can peer into an audience, social media seems most enticing. The breadth of subjects, range of observations, and, above all, the ability to connect and draw inferences make social analytics hugely exciting for anyone who is interested in understanding and influencing past, present and potential customers, employees, or even investors.

As individuals leave traces of their activities - personal, social and professional - on the internet, they allow an unprecedented view into their lives, thoughts, influences and preferences. Social analytics attempts to draw useful understanding and inferences, which could be relevant to marketers, sales persons, HR managers, product designers, investors and so on. Thus, as social tools like Facebook, Twitter, LinkedIn, WhatsApp, and many more, host a plethora of social activities of many people, a humongous amount of data is generated about people's preferences, behaviour and sentiments. Like any data, it is amenable to analysis to gain useful insights.

The challenge comes from the sheer volume, velocity, and variety. It is very difficult to ensure that the analysis is relevant and reliable. Besides the daunting technical intricacies of setting up the appropriate analytics, the aspects of choosing information sources, filtering the right data, and its interpretation and aggregation are susceptible to errors and biases. For example, some social activities are relatively easier to access (like activity on Twitter, or public updates on Facebook), many are not. Some types of data (like text, or location) are easy to search and interpret, many (like pictures) are not. So a good analysis model must judiciously compensate for the nature of the sources included, and hence it could be at times very difficult to assess if the analysis is useful or just meaningless mumbo-jumbo.

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