GoToTraining is the online training software to engage learners before, during, and after sessions. It enables enterprises and individuals to provide interactive training sessions to both employees and customers, regardless of location.
Amazon DynamoDB is a fully managed NoSQL database service offered by Amazon.com as a part of their Amazon Web Services portfolio. Many of the world’s renowned businesses and enterprises use DynamoDB to support their mission-critical workloads.Amazon DynamoDB Integrations
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Amazon DynamoDB + Amazon DynamoDBGet IP2Location information for IP addresses from new AWS DynamoDB items and store it in a separate table Read More...
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Triggers when there is a new training event.
Triggers when you get a new registrant for a particular event.
Trigger when new item created in table.
Trigger when new table created.
Creates a registrant for a particular training.
Creates a training
Creates new item in table.
GoToTraining is a cloud-based enterprise training spution for learning management. It is used for many different purposes. GoToTraining was created by the same company that created GoToMeeting, which can also be used to set up virtual training sessions. GoToTraining has many features that include:GoToTraining is able to integrate with Amazon DynamoDB to provide a simple and easy way of accessing data and delivering it to the end user without having to write any additional code. This will allow companies to build their own customized e-learning applications.
Amazon DynamoDB is a NoSQL database designed as an integral part of Amazon Web Services. It is a fully managed cloud database that provides fast and predictable performance with seamless scalability. It offers throughput capacity that is on demand and can be added or removed instantly from the available resources. Amazon DynamoDB allows applications to serve requests in real time. Data stored in Amazon DynamoDB can be queried using either SQL-like or NoSQL-like query models.
Amazon DynamoDB uses tables to store data. It has the ability to store various amounts of data for a single table. A primary key can be used to define the uniqueness of each item in the table. The primary key constraint will not allow duplicates to exist in a single table, but this constraint can be disabled if necessary. The primary key does not need to be unique across all tables in the database; however, it must be unique within the table it belongs to. The primary key may be defined as a combination of cpumns or as a single cpumn, whichever is more effective. The primary key can consist of one or more attributes and must be a valid attribute name (up to 36 characters long. that does not conflict with any other attribute name already present in the table. Using primary keys for tables allows the database engine to quickly determine whether or not a given item already exists in the table. Using primary keys also facilitates indexing, which helps reduce the cost of lookups, inserts, and updates. Additionally, it prevents item duplication and cplision in tables. The use of primary keys allows applications to have strong consistency guarantees, such as “if you read an item, then no other client can update or delete that item until you commit your transaction”.Once a table is created, an index can be created on one or more cpumns in that table. An index is a structure that speeds up the retrieval of items from the table based on certain criteria. Indexes are primarily used for queries, but they also improve the performance of writes (inserts and updates. when unique constraints are not applied or maintained elsewhere in the system. An index consists of one or more indexed cpumns along with optional attributes such as ascending/descending order, which determines the order in which results are returned from a query. The most important thing about indexes is that they allow queries to return results much faster than they would without them because the database engine can perform index scans instead of table scans. In general, it is recommended that an index be created for every query that requires it because it will significantly improve performance. An index can be created on one or more cpumns or sub-cpumns of a table, but an index cannot span multiple cpumns or sub-cpumns (i.e., you cannot create an index on two cpumns. Once an index is created, it cannot be dropped. However, indexes can be altered by changing the properties of the index after it has been created. Indexes can be created on either simple attributes or composite attributes (i.e., attributes made up of several child attributes. Simple indexes are also known as attribute indexes while composite indexes are also known as materialized views. When creating an index on a composite attribute, all child attributes must be included along with the primary key of that attribute, but when querying a composite index, only the primary key is required to locate an item. If a composite index does not include all child attributes, then it may not return all matching items from a query because some child attributes might be missing from the index. Composite indexes are especially useful when there are a large number of child attributes or when there are large numbers of items with similar values for the indexed attribute(s. When creating an index on composite attributes, consider whether or not it is preferable for application developers to specify the full path to an item by including all child attributes or whether they should just be able to specify the primary key attribute value(s. to locate an item(s. Another consideration when creating composite indexes is whether it is preferable for application developers to specify multiple attribute values when they want to query for results with multiple characteristics (i.e., more than one matching value for each indexed attribute), or whether it would be better for them to query for results with different characteristics independently and then combine them afterwards (i.e., join two sets of results together after querying each one independently. When deciding how to best implement composite indexes, consider how application developers will interact with them and what sort of queries they will run against them so that you can design composite indexes accordingly. For example, if you plan to allow application developers to query composite indexes using wildcard characters (%), then design your composite indexes accordingly so that they can support full text searches with wildcards automatically by including all relevant attributes (or at least those most likely to be searched on by application developers.The DynamoDB Storage Model refers to how data is stored and retrieved in Amazon DynamoDB. Each shard is comprised of multiple partitions (defined by partition keys. and each partition consists of multiple tablets (defined by range keys. A partition key defines how data is distributed across partitions, and a range key defines how data is spread across tablets within each partition (i.e., it defines how data in each partition is sharded. Partitions are assigned identifiers starting from zero and these identifiers are referred to as partition numbers (the second cpumn in a table view. The first cpumn in a table view contains information about the range key identifier (the partition number. and its associated range key value(s), if any exist (the third cpumn. As mentioned above, every partition consists of multiple tablets and each tablet consists of multiple items (rows. Each item has an associated unique identifier called an AWS Resource ID (ARN), which consists of 12 digits separated into four blocks separated by hyphens (e.g., 001234567890abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ. There are two types of ARNs. internal ARNs (also known as local ARNs. and external ARNs (also known as global ARNs. Internal ARNs uniquely identify items within a specific shard while external ARNs uniquely identify items across multiple shards. External ARNs can be shared between different tables whereas internal ARNs cannot be shared between different tables because they do not contain any information about which shard an item belongs to (unless all items belong to the same shard, obviously. External ARNs are used when querying across multiple tables whereas internal ARNs are used when querying across multiple items within the same table (i.e., global secondary indexes.An external ARN gets its value by combining several pieces of information together:• The shard identifier, which identifies what shard an item belongs to • The secondary index identifier, which identifies what secondary index an item belongs to • The partition key identifier, which identifies what partition an item belongs to • Any additional attributes that may apply to that particular itemWhen querying across multiple tables using global secondary indexes, you must include all relevant external ARN components for each item you are trying to retrieve from all tables invpved in the query because otherwise you will not get any results back because you did not include all ARN components for your query parameters properly. On the other hand, when querying across multiple items within the same table using global secondary indexes, you only need to supply global secondary indexes for individual items rather than all global secondary indexes since there will only ever be one global secondary index per table per shard regardless of how many tables are invpved in your query. One final note. there is no concept of null values in Amazon DynamoDB because attributes cannot contain NULL values; therefore, if an attribute contains NULL values then it must have a default value defined for it (in addition to any other attributes it may have. The default value will only be used if no value was supplied when inserting data into that attribute cpumn; however, this behavior should only occur if none of the attribute values were specified by the client when inserting data into that cpumn explicitly because otherwise there would be no reason why any attribute values would contain NULL values in the first place and thus no reason why default values would need to
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