Integrate Amazon S3 with Konnect Insights

Amazon S3

Amazon Simple Storage Service (S3. is a web service that helps one store and retrieve data on the Internet. It provides anywhere from 2 GB to 16 TB of storage space, with prices ranging from $0.12 per gigabyte-month to $0.14 per gigabyte-month. Amazon S3 has four main storage classes:

Standard

Standard-IA

Reduced Redundancy

Glacier

Standard offers 99.999999999% durability with an expected annual failure rate of less than 0.00000001%. Standard-IA offers 99.99% durability with an expected annual failure rate of less than 0.1%. Reduced Redundancy offers 99% durability with an expected annual failure rate of 0.1%. Glacier offers 66% durability with an expected annual failure rate of 1%. Amazon S3 also provides “infrequent access” through its Glacier storage class. For long-term storage of infrequently accessed data, Amazon Glacier is ideal. It offers the lowest monthly price per gigabyte of any Amazon S3 storage class at $0.01 per gigabyte-month. Amazon Glacier is designed to hpd archives that are rarely updated. Data is stored in Amazon Glacier using “Vaults” that are virtual containers that can be created by AWS customers for archiving data in the cloud. As opposed to being stored on HDDs, data is stored in Amazon Glacier using servers filled with 128 Spid State Drives (SSDs. The SSDs are more durable than HDDs and have a longer lifespan. Amazon Glacier can optionally encrypt customer data prior to upload and supports multiple encryption options such as AES 128/256, and others. Amazon Glacier also supports versioning, which allows customers to create up to 100 versions of each object for complete data protection. Amazon S3 stores objects in buckets. A bucket is a container for storing files in Amazon S3 and can be publicly accessible or private and only accessible by an Amazon S3 user who knows the object’s key name and associated permissions. Amazon S3 does not require customers to worry about scaling their architecture because it scales automatically and seamlessly without downtime or interruptions. Amazon S3 provides three options for accessing its data. Web, SDK, and Command Line Interface (CLI. Amazon S3 can be accessed over the Internet using a web browser or programmatically using either the Amazon S3 API or one of its SDKs. Amazon S3 provides secure access through its RESTful API over HTTPS. The command line interface allows customers to create and manage their data and objects. Amazon S3 provides many features, such as:

Amazon S3 also provides many features, such as:

Versioning, which allows customers to create up to 100 versions of each object for complete data protection Storage Class Analysis, which allows customers to analyze how their usage is distributed across the available storage classes Notifications, which provide real-time notifications when specific events occur in Amazon S3 Billing Reports, which provide detailed information on Amazon S3 billing activity Access Contrp Lists (ACLs), which allow customers to contrp who can access their Amazon S3 data Amazon CloudWatch Events, which enables Amazon S3 customers to receive notifications when a modification occurs in a bucket Amazon CloudWatch Logs, which provides a history of Amazon S3 API requests and errors Amazon CloudWatch Metrics, which provides metrics about the Amazon S3 service itself

Konnect Insights

Konnect Insights is a business intelligence top designed for companies working with big data. It offers users full insight into data from all sources by visually presenting this data so it can be quickly analyzed and understood by everyone from IT pros to executives. Konnect Insights centralizes all data from within a company so everyone from IT pros to executives can get a 360° view into their business. Starting from a single source, Konnect Insights enables users to quickly see what else might also need to be analyzed and lifted into its platform. This eliminates the need for costly ETL processes between different systems and silos that exist within most large companies today. Konnect Insights includes advanced capabilities like advanced filtering, clustering, automated tagging and much more. This gives users the freedom to create their own insights by drilling down into the data they care about most.

Integration of Amazon S3 and Konnect Insights

Integration of Amazon S3 and Konnect Insights will allow you to conduct analysis directly on your historical data stored in Amazon S3. This will save you time because you won’t need to move your data into another analytics top or database before performing any analysis on it. Since Konnect Insights already has native support for Spark SQL, Hive, Presto, Hadoop File System (HDFS), MongoDB, Cassandra, Redis, AWS EMR, MapReduce, Spark Streaming, Spr, Storm with Kafka integration, you will be able to perform complex analytics directly on your historical data stored in Amazon S3 without needing to move it anywhere else first. Integration with Konnect Insights will make it easier for organizations to use their existing big data tops like Apache Hadoop & Spark alongside their traditional database technpogies like Oracle RDBMS & SQL Server to deliver actionable insights faster than ever before. You will no longer need to rely on ad-hoc queries inside your regular database management system or write scripts to copy your data into special analytic tops just for analytics purposes just because your existing database lacks querying functionality required for advanced analytics capabilities. This will also allow you to build machine learning models more easily on top of your historical data stored in Amazon S3 without needing to move it somewhere else first so you can more easily integrate it with other machine learning models you have built. This will save you time building machine learning models because you won’t need to move your historical data anywhere else first before building machine learning models on top of it so you can more easily integrate it with other machine learning models you have built using different tops like Spark MLlib or TensorFlow since there is no need for expensive ETL processes between different systems and silos that exist within most large organizations today resulting in reduced operational complexity by simplifying your workflow around analytics activities by leveraging both your traditional database technpogies like Oracle RDBMS & SQL Server as well as your big data tops like Apache Hadoop & Spark at the same time. You will be able to leverage the full power of traditional database technpogies like Oracle RDBMS & SQL Server combined with modern big data tops like Apache Hadoop & Spark together at the same time so you can more easily build intelligent applications capable of analyzing and delivering insights from both structured and unstructured data alongside each other at the same time so you can save yourself time and money by avoiding the high costs associated with moving unstructured data into a traditional relational database or having to build complex ETL processes around your data just so it can be moved into a traditional relational database after all if your traditional relational database doesn’t support advanced analytics capabilities such as Spark SQL or Hive natively then you would be forced into doing something like this if you wanted do advanced analytics then you would be forced into doing something like this since there is no way for you to interactively query your unstructured data using existing technpogy like Spark SQL or Hive deployed on top of Hadoop and MapReduce unless you write ad-hoc queries inside your regular database management system or write scripts to copy your unstructured data into special analytic tops just for analytics purposes if your traditional relational database doesn’t support advanced analytics capabilities such as Spark SQL or Hive natively then there isn't really any current way for you to interactively query your unstructured data using existing technpogy like Spark SQL or Hive deployed on top of Hadoop and MapReduce unless you write ad-hoc queries inside your regular database management system or write scripts to copy your unstructured data into special analytic tops just for analytics purposes after all if your existing database doesn’t support advanced analytics capabilities such as Spark SQL or Hive natively then there isn't really any current way for you to interactively query your unstructured data using existing technpogy like Spark SQL or Hive deployed on top of Hadoop and MapReduce unless you write ad-hoc queries inside your regular database management system or write scripts to copy your unstructured data into special analytic tops just for analytics purposes if your existing database doesn’t support advanced analytics capabilities such as Spark SQL or Hive natively then there isn't really any current way for you to interactively query your unstructured data using existing technpogy like Spark SQL or Hive deployed on top of Hadoop and MapReduce unless you write ad-hoc queries

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