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The ATLAS EventIndex has been in operation since the beginning of LHC Run 2 in 2015. Like all software projects, its components have been constantly evolving and improving in performance. The main data store in Hadoop, based on MapFiles and HBase, can work for the rest of Run 2 but new solutions are explored for the future. Kudu offers an interesting environment, with a mixture of BigData and relational database features, which look promising at the design level. This environment is used to build a prototype to measure the scaling capabilities as functions of data input rates, total data volumes and data query and retrieval rates. In this proceedings we report on the selected data schemas and on the current performance measurements with the Kudu proto-

The ATLAS EventIndex was designed in 2012-2013 to provide a global event catalogue and limited event-level metadata for ATLAS analysis groups and users during the LHC Run 2 (2015-2018). It provides a good and reliable service for the initial use cases (mainly event picking) and several additional ones, such as production consistency checks, duplicate event detection and measurements of the overlaps of trigger chains and derivation datasets. The LHC Run 3, starting in 2021, will see increased data-taking and simulation production rates, with which the current infrastructure would still cope but may be stretched to its limits by the end of Run 3. This proceeding describes the implementation of a new core storage service that will be able to provide at least the same functionality as the current one for increased data ingestion and search rates, and with increasing volumes of stored data. It is based on a set of HBase tables, with schemas derived from the current Oracle implementation, coupled to Apache Phoenix for data access; in this way we will add to the advantages of a BigData based storage system the possibility of SQL as well as NoSQL data access, allowing to re-use most of the existing code for metadata integration.