Abstract: A method may include identifying an identifier field included in a first datatype of a seed data sample associated with a source system. The identifier field may store a first value that enables a differentiation between different instances of the first datatype. A relationship field, which stores a second value that define a relationship between the first datatype and a second data type, may be identified. A synthetic data sample may be generated by populating the identifier field of the synthetic data sample with a synthetically generated value and the relationship field of the synthetic data sample with the second value. The synthetic data sample may be sent to a target system to enable a performance of a task at the target system. The synthetic data sample may supplement a volume and/or a diversity of the data that occurs organically at the source system.

Abstract: A method may include allocating, based on a first load requirement of a first tenant, a first bin having a fixed capacity for handing the first load requirement of the first tenant. In response to the first load requirement of the first tenant exceeding a first threshold of the fixed capacity of the first bin, packing a second bin allocated to handle a second load requirement of a second tenant. The second bin may be packed by transferring, to the second bin, the first load requirement of the first tenant based on the transfer not exceeding the first threshold of the fixed capacity of the second bin. In response to the transfer exceeding the first threshold of the fixed capacity of the second bin, allocating a third bin to handle the first load requirement of the first tenant.

Abstract: A method may include receiving, at a data lake platform, a packet including a metadata corresponding to a data schema of a source system. A change in the data schema of the source system may be detected based on a first checksum of the metadata and a second checksum of a previous version of the metadata. In response to detecting the change in the data schema of the source system, the metadata may be sent to a target system to enable the target system to perform, based on the data schema of the source system, a task operating on a data from the source system. The task may include reporting, visualization, advanced analytics, and/or machine learning. Related systems and computer program products are also provided.

Abstract: A search engine may detect a user selecting an object associated with a first category subsequent to inputting a first search phrase including a keyword. In response, the search engine may update a learning model by at least incrementing a relevance score for an association between the keyword and the first category. The search engine may suggest keywords for completing a second search phrase based on the updated learning model. The search engine may further respond to the second search phrase by determining, based on the updated learning model, that the first category is more relevant to the first user than a second category. A search result of the second search phrase may be refined by eliminating, from objects matching the second search phrase, objects associated with the second category but not the first category.

Abstract: A search engine may detect a user selecting an object associated with a first category subsequent to inputting a first search phrase including a keyword. In response, the search engine may update a learning model by at least incrementing a relevance score for an association between the keyword and the first category. The search engine may suggest keywords for completing a second search phrase based on the updated learning model. The search engine may further respond to the second search phrase by determining, based on the updated learning model, that the first category is more relevant to the first user than a second category. A search result of the second search phrase may be refined by eliminating, from objects matching the second search phrase, objects associated with the second category but not the first category.