TY - CHAP A1 - Klettke, Meike A1 - Awolin, Hannes A1 - Störl, Uta A1 - Müller, Daniel A1 - Scherzinger, Stefanie A1 - Storl, Uta A1 - Muller, Daniel T1 - Uncovering the evolution history of data lakes T2 - 2017 IEEE International Conference on Big Data (Big Data),,11-14 Dec. 2017, Boston, MA, USA N2 - Data accumulating in data lakes can become inaccessible in the long run when its semantics are not available. The heterogeneity of data formats and the sheer volumes of data collections prohibit cleaning and unifying the data manually. Thus, tools for automated data lake analysis are of great interest. In this paper, we target the particular problem of reconstructing the schema evolution history from data lakes. Knowing how the data is structured, and how this structure has evolved over time, enables programmatic access to the lake. By deriving a sequence of schema versions, rather than a single schema, we take into account structural changes over time. Moreover, we address the challenge of detecting inclusion dependencies. This is a prerequisite for mapping between succeeding schema versions, and in particular, detecting nontrivial changes such as a property having been moved or copied. We evaluate our approach for detecting inclusion dependencies using the MovieLens dataset, as well an adaption of a dataset containing botanical descriptions, to cover specific edge cases. KW - Data mining KW - evolution operations KW - Grippers KW - history KW - inclusion dependencies KW - integrity constraints KW - Lakes KW - NoSQL databases KW - Protocols KW - schema version extraction Y1 - 2017 U6 - https://doi.org/10.1109/BigData.2017.8258204 SP - 2462 EP - 2471 PB - IEEE ER - TY - CHAP A1 - Klettke, Meike A1 - Störl, Uta A1 - Shenavai, Manuel A1 - Scherzinger, Stefanie A1 - Storl, Uta T1 - NoSQL schema evolution and big data migration at scale T2 - 2016 IEEE International Conference on Big Data (Big Data), 5-8 Dec. 2016, Washington, DC N2 - This paper explores scalable implementation strategies for carrying out lazy schema evolution in NoSQL data stores. For decades, schema evolution has been an evergreen in database research. Yet new challenges arise in the context of cloud-hosted data backends: With all database reads and writes charged by the provider, migrating the entire data instance eagerly into a new schema can be prohibitively expensive. Thus, lazy migration may be more cost-efficient, as legacy entities are only migrated in case they are actually accessed by the application. Related work has shown that the overhead of migrating data lazily is affordable when a single evolutionary change is carried out, such as adding a new property. In this paper, we focus on long-term schema evolution, where chains of pending schema evolution operations may have to be applied. Chains occur when legacy entities written several application releases back are finally accessed by the application. We discuss strategies for dealing with chains of evolution operations, in particular, the composition into a single, equivalent composite migration that performs the required version jump. Our experiments with MongoDB focus on scalable implementation strategies. Our lineup further compares the number of write operations, and thus, the operational costs of different data migration strategies. KW - Big data KW - Context KW - Data Migration Strategies KW - Data models KW - Databases KW - Incremental Migration KW - Lazy Composite Migration KW - Lazy Migration KW - NoSQL databases KW - Predictive Migration KW - Production KW - Runtime KW - schema evolution KW - Software Y1 - 2016 U6 - https://doi.org/10.1109/BigData.2016.7840924 SP - 2764 EP - 2774 PB - IEEE ER - TY - CHAP A1 - Hillenbrand, Andrea A1 - Levchenko, Maksym A1 - Störl, Uta A1 - Scherzinger, Stefanie A1 - Klettke, Meike ED - Boncz, Peter ED - Manegold, Stefan ED - Ailamaki, Anastasia ED - Deshpande, Amol ED - Kraska, Tim T1 - MigCast : Putting a Price Tag on Data Model Evolution in NoSQL Data Stores T2 - Proceedings of the 2019 International Conference on Management of Data (SIGMOD/PODS '19) June 2019, Amsterdam, Netherlands N2 - We demonstrate MigCast, a tool-based advisor for exploring data migration strategies in the context of developing NoSQL-backed applications. Users of MigCast can consider their options for evolving their data model along with legacy data already persisted in the cloud-hosted production data-base. They can explore alternative actions as the financial costs are predicted respective to the cloud provider chosen. Thereby they are better equipped to assess potential consequences of imminent data migration decisions. To this end, MigCast maintains an internal cost model, taking into account characteristics of the data instance, expected work-load, data model changes, and cloud provider pricing models. Hence, MigCast enables software project stakeholders to remain in control of the operative costs and to make informed decisions evolving their applications. KW - Data Migration Strategies KW - latency KW - migration costs KW - NoSQL databases KW - Predictive Migration KW - schema evolution Y1 - 2019 SN - 9781450356435 U6 - https://doi.org/10.1145/3299869.3320223 SP - 1925 EP - 1928 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Störl, Uta A1 - Tekleab, Alexander A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Storl, Uta T1 - In for a Surprise When Migrating NoSQL Data T2 - 2018 IEEE 34th International Conference on Data Engineering (ICDE), 16-19 April 2018, Paris, France N2 - Schema-flexible NoSQL data stores lend themselves nicely for storing versioned data, a product of schema evolution. In this lightning talk, we apply pending schema changes to records that have been persisted several schema versions back. We present first experiments with MongoDB and Cassandra, where we explore the trade-off between applying chains of pending changes stepwise (one after the other), and as composite operations. Contrary to intuition, composite migration is not necessarily faster. The culprit is the computational overhead for deriving the compositions. However, caching composition formulae achieves a speed up: For Cassandra, we can cut the runtime by nearly 80%. Surprisingly, the relative speedup seems to be system-dependent. Our take away message is that in applying pending schema changes in NoSQL data stores, we need to base our design decisions on experimental evidence rather than on intuition alone. KW - composite migration KW - Conferences KW - Data engineering KW - data migration KW - Indexes KW - Lightning KW - NoSQL databases KW - Runtime KW - schema evolution KW - Tools Y1 - 2018 U6 - https://doi.org/10.1109/ICDE.2018.00202 SP - 1662 PB - IEEE ER - TY - CHAP A1 - Störl, Uta A1 - Müller, Daniel A1 - Tekleab, Alexander A1 - Tolale, Stephane A1 - Stenzel, Julian A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Storl, Uta A1 - Muller, Daniel T1 - Curating Variational Data in Application Development T2 - 2018 IEEE 34th International Conference on Data Engineering, 16-19 April 2018, Paris, France N2 - Building applications for processing data lakes is a software engineering challenge. We present Darwin, a middleware for applications that operate on variational data. This concerns data with heterogeneous structure, usually stored within a schema-flexible NoSQL database. Darwin assists application developers in essential data and schema curation tasks: Upon request, Darwin extracts a schema description, discovers the history of schema versions, and proposes mappings between these versions. Users of Darwin may interactively choose which mappings are most realistic. Darwin is further capable of rewriting queries at runtime, to ensure that queries also comply with legacy data. Alternatively, Darwin can migrate legacy data to reduce the structural heterogeneity. Using Darwin, developers may thus evolve their data in sync with their code. In our hands-on demo, we curate synthetic as well as real-life datasets. KW - data migration KW - Data mining KW - Evolution (biology) KW - history KW - NoSQL databases KW - query rewriting KW - schema evolution KW - schema management KW - Software KW - Task analysis KW - variational data Y1 - 2018 U6 - https://doi.org/10.1109/ICDE.2018.00187 SP - 1605 EP - 1608 PB - IEEE ER -