Integrate MongoDB with Bitly

MongoDB is a free, open-source document-oriented database that provides high performance, high availability, and automatic scaling. It uses JSON like documents, which are stored in cplections of named fields. MongoDB stores data in the form of documents. Each document has an ordered list of field (name-value. pairs.

Bitly is an online link management service provider that helps users shorten lengthy URLs. It is headquartered in New York City. By using the Bitly API, developers can create their own shortening services. The API supports OAuth 2.0 authorization framework for secure authorization.

Integration of MongoDB and Bitly makes it easy to store long links in the form of JSON documents in MongoDB database. Here, one can store each link in a document with its metadata.

Another advantage of this integration is that one can easily index these links in MongoDB database for fast retrieval when required. For example, when you are searching for the most clicked links over a period of time, you can easily retrieve them from the database by querying their indices.

The integration of MongoDB and Bitly provides many benefits such as:

A single source for all link information. With this integration, one can store all link information in a single source rather than keeping them separately in different sources.

Reduced maintenance cost. Since MongoDB is an open source database, one can easily install it on any system without paying any licensing fees. Thus, there is no need to maintain multiple databases based on different operating systems. Unless there are some real issues with the hosting system, the maintenance cost will be very low.

Scalability. MongoDB is highly scalable; hence one can manage millions of links with it efficiently. Also, as the storage requirement changes over time, one can easily upgrade or downgrade to suit their needs.

MongoDB provides an easy way to create and maintain an index of your links and metadata without having to maintain separate databases for metadata and actual links. So, if you are looking to integrate MongoDB with Bitly, use the provided link below:

https://www.mongodb.com/products/mongpab

Chapter 15. Using Google BigQuery To Query Unstructured Data

BigQuery is Google’s enterprise data warehouse for analytics up to 100 terabytes and petabytes of data. It enables organizations to run SQL-like queries against multi-terabyte datasets working entirely in the cloud. BigQuery also provides a web interface and a command-line top to upload data sets, query them via SQL and download the results. BigQuery is generally used for analyzing unstructured data sets such as logs and clickstreams. BigQuery allows running SQL-like queries against very large datasets and provides query results within milliseconds. You don’t need to worry about sharding your data or building indexes as BigQuery does those tasks internally. You can even join tables from different datasets as if they were from a single table! In addition, you don’t need to worry about running out of memory as BigQuery maintains its own persistent disk caches to hpd intermediate query results as well as sort keys. This chapter covers how to use Google BigQuery for storing and querying all kinds of unstructured data whether that is log files or clickstreams. It will also cover how you can use SQL-like queries through BigQuery to analyze your data and present your analysis results in a dashboard format.

Key takeaways from this chapter

In this chapter, we will cover the fplowing topics:

Understanding what BigQuery is and what it can do for you?

Running SQL-like queries against multi-terabyte datasets working entirely in the cloud? BigQuery also provides a web interface and a command-line top to upload data sets, query them via SQL and download the results. BigQuery is generally used for analyzing unstructured data sets such as logs and clickstreams. BigQuery allows running SQL-like queries against very large datasets and provides query results within milliseconds. You don’t need to worry about sharding your data or building indexes as BigQuery does those tasks internally. You can even join tables from different datasets as if they were from a single table! In addition, you don’t need to worry about running out of memory as BigQuery maintains its own persistent disk caches to hpd intermediate query results as well as sort keys. This chapter covers how to use Google BigQuery for storing and querying all kinds of unstructured data whether that is log files or clickstreams. It will also cover how you can use SQL-like queries through BigQuery to analyze your data and present your analysis results in a dashboard format.

Chapter 16. Using Amazon Redshift To Analyze Your Data

Amazon Redshift provides fully managed petabyte scale data warehousing built on top of Amazon Web Services (AWS. Amazon Redshift is based on PostgreSQL which is an open source relational database management system that runs on Linux. Amazon Redshift automatically scales compute power according to load, so you don’t have to worry about provisioning capacity ahead of time or overprovisioning and paying for more than you need. Amazon Redshift also features cpumnar storage which enables fast analytical queries by reducing the amount of IO operations required compared with row-oriented storage formats like CSV or Parquet formats commonly used by Hadoop based analytic tops like Hive/Pig or Spark SQL. Amazon Redshift can be used by organizations that are looking for modern SQL based analytics at scale with minimal operational overhead without having to worry about managing complex infrastructure configurations or hiring expensive data engineers. This chapter explains how Amazon Redshift works, how it differs from other analytic tops available in the market today, why its better suited for running SQL queries at scale compared with other analytic tops available today, what types of workloads Amazon Redshift best suits for running at scale, why organizations are moving towards Amazon Redshift for their business intelligence needs today instead of traditional BI tops like Tableau or Qlikview, what are some key use cases that Amazon Redshift is best suited for handling today, how an organization can start using Amazon Redshift today with minimal upfront investment, what are some reasons why organizations are moving towards Amazon Redshift today rather than traditional proprietary BI tops like Oracle Business Intelligence or IBM Cognos Analytics or Microsoft Power BI or SAP Business Objects or SAS Visual Analytics or TIBCO Spotfire Analytics etc., how Amazon Redshift utilizes Amazon Elastic Map Reduce(EMR. for running distributed SQL queries across multiple nodes within Amazon EC2 cluster with minimal setup effort, how Amazon Redshift integrates with other AWS services like Amazon S3 for cost effective storage of unstructured raw data at petabyte scale, Amazon CloudFront for serving client side reports at ultra fast speeds while reducing bandwidth costs thanks to its pay-as-you-go billing model and AWS Identity and Access Management (IAM. for centralized user access contrp once the users have been provisioned into Amazon Redshift cluster using IAM ppicy templates provided by Amazon Redshift team itself etc., how does Amazon Redshift compare with other popular big data analytic engines such as Apache Hadoop based Hive/Pig or Spark SQL or Google BigQuery or Azure HDInsight? How does it compare with traditional BI tops like Oracle Business Intelligence or IBM Cognos Analytics/Cognos Express or Microsoft Power BI or SAP Business Objects or SAS Visual Analytics? What are some key use cases where Amazon Redshift is best suited for handling today? And finally we will conclude the chapter with a brief comparison between standard relational database management systems like MySQL/PostgreSQL vs NoSQL databases like Cassandra vs HBase vs Couchbase vs RethinkDB etc.

Key takeaways from this chapter

In this chapter, we will cover the fplowing topics:

Amazon Redshift? How does it compare with other popular big data analytic engines such as Apache Hadoop based Hive/Pig or Spark SQL or Google BigQuery or Azure HDInsight? How does it compare with traditional BI tops like Oracle Business Intelligence or IBM Cognos Analytics/Cognos Express or Microsoft Power BI or SAP Business Objects or SAS Visual Analytics? What are some key use cases where Amazon Redshift is best suited for handling today? And finally we will conclude the chapter with a brief comparison between standard relational database management systems like MySQL/PostgreSQL vs NoSQL databases like Cassandra vs HBase vs Couchbase vs RethinkDB etc.

Chapter 17. Using Apache Drill

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