Download Big Data Application Architecture Q&A: A Problem - Solution by Nitin Sawant, Himanshu Shah PDF

By Nitin Sawant, Himanshu Shah

Great facts software structure trend Recipes presents an perception into heterogeneous infrastructures, databases, and visualization and analytics instruments used for knowing the architectures of massive facts strategies. Its problem-solution strategy is helping in selecting the best structure to resolve the matter handy. within the strategy of analyzing via those difficulties, you'll examine harness the facility of latest vast facts possibilities which a number of firms use to achieve real-time gains.

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Extra info for Big Data Application Architecture Q&A: A Problem - Solution Approach

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EMC comes with a similar appliance called Greenplum with similar features to facilitate high speed low cost data processing using Hadoop. Problem How do I ingest data onto third-party public cloud options like Google BigQuery? Solution BigQuery is Google’s cloud-based big data analytics service. You can upload your big data set to BigQuery for analysis. However, depending on your data’s structure, you might need to prepare the data before loading it into BigQuery. For example, you might need to export your data into a different format or transform the data.

These appliances also ensure that instead of aligning with multiple vendors for software, storage and tools, only a tie-up with a single vendor is needed. This reduces considerable legwork for the client, ensuring the client deals with a single vendor for all issues. HP Vertica (Figure 4-7) is an example of an all-in-one appliance. Entity Vendor tie-ups Cloudera, Hortonworks or MAPR distribution Hadoop Software Distribution HP Vertica – RAID compliant columnar database Storage Infrastructure HP Proliant servers Analytics/Visualization SAS Machine Learning R Figure 4-7.

Along similar lines, patterns for data access are also governed by the application functionality. As you can see in Table 5-1, data should be abstracted in a layer above the Hadoop distributed file system (HDFS) to ensure low latency and business-specific data storage in a structured format. Table 5-1. Use Cases to Access Patterns Sr. No Use Cases Access Pattern 1 Bulk data Connector 2 Search Near Real-Time, Stage Transform 3 Data Visualization Stage Transform plus Connector 4 Reports Stage Transform 5 Data Discovery Lightweight Stateless 6 Enterprise-Wide Dashboard Service Locator Different patterns are suitable for different types of use cases: • The Connector pattern is typically used to process bulk data in XML form.

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