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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. layout: publication title: Publication subtitle: > Rheem link-name: Tutorial at BOSS 2016 img-thumb: assets/img/screenshot/rheemhandson.png authors: Zoi Kaoudi, Sebastian Kruse and Jorge-Arnulfo Quiané-Ruiz year: 2016 month: 09 day: 05 link-paper: assets/pdf/paper/rheemhandson.pdf link-external: false

Bored of keep moving your app to the newest data processing platform to achieve high performance? Tired of dealing with a zoo of processing platforms to get the best performance for your analytic tasks? Then, this tutorial is for you!

Indeed, we are witnessing a plethora of innovative data processing platforms in the last few years. While this is generally great, leveraging these new technologies in practice bears quite some challenges, just to name a few, developers must: (i) find among the plethora of processing platforms the best one for their applications; (ii) migrate their applications to newer and faster platforms every now and then; and (iii) orchestrate different platforms so that applications leverage their individual benefits.

We address these issues with Rheem, a system that enables big data analytics over multiple data processing platforms in a seamless manner. It provides a three-layer data processing abstraction with that applications can achieve both platform independence and interoperability across multiple platforms. With Rheem, dRheemers (Rheem developers) can focus on the logics of their applications. Rheem, in turn, takes care of efficiently executing applications by choosing either a single or multiple processing platforms. To achieve this, it comes with a cross-platform optimizer that allows a single task to run faster over multiple platforms than on a single platform.