Data Management Built for the Cloud & AI Era: WEKA

WEKA, as a forward-thinking organization, firmly holds the belief that modern data-driven enterprises should never be compelled to compromise on critical aspects such as speed, simplicity, scale, or sustainability when handling and analyzing their data. In the current era of cloud computing and artificial intelligence, these facets must constitute the fundamental pillars of every data environment, devoid of any compromises. This unwavering principle is what propels WEKA to develop a software-defined, hybrid cloud-based approach to data management, one that seamlessly caters to the demands of artificial intelligence, machine learning, high-performance computing, and other next-generation workloads. WEKA's journey commenced in 2013 when the landscape of performance-intensive workloads in enterprises was undergoing a significant transformation. Armed with a blank canvas, a novel programming language, and a vision for a groundbreaking data management approach, the company's founders embarked on a mission to eradicate the compromises of the past and lay the foundation for a future in which data powers innovation. Enter the WEKA® Data Platform, meticulously crafted to deliver speed, simplicity, and scalability to modern enterprises and research organizations without compromise. Its sophisticated, software-defined architecture ensures that it can seamlessly accommodate next-generation workloads, whether they are hosted in the cloud or on-premises.

The speed it provides is nothing short of extraordinary, the simplicity is truly enticing, scalability is virtually limitless, and sustainability is effortlessly woven into the fabric of the platform. The WEKA Data Platform transforms conventional data storage silos into dynamic data pipelines that serve as the lifeblood for AI, ML, and HPC workloads. Furthermore, it transcends physical boundaries, seamlessly integrating with edge, core, cloud, hybrid, and multicloud data environments. WEKA is not just a solution; it's a catalyst for a new era of sustainable data innovation in the world's leading research organizations and enterprises. It empowers them to keep pace with the speed of scientific advancement, to unleash creativity without constraints, and to turn innovative ideas into tangible outcomes. In essence, WEKA is not just a company; it's a driving force behind the transformation of data management, making it an asset rather than a hindrance, and turning it into a powerful tool for businesses and researchers. The company is dedicated to helping organizations navigate this data-driven world seamlessly and achieve their goals with unparalleled speed, simplicity, and sustainability.

Unlock innovation 

Organizations are eliminating the complexity of legacy data storage infrastructure and building data pipelines on data management platforms. A data management platform is an integrated, end-to-end solution that provides holistic support for an organization’s data management needs while supporting every step of the organization’s data lifecycle – from ingest and pre-processing to analyzing, storage, and archiving. A true data management platform is designed to support both the structured and unstructured data a digital organization uses, regardless of whether the data is at the core, cloud, or edge. It is multi-tenant, multi-workload, multi-performant, and multi-location, all with a common management interface.

Putting Pipelines into Operation is as Critical as Building Them

Operationalizing data pipelines presents a multifaceted challenge, a challenge whose significance is often underestimated. The process of crafting these pipelines is akin to constructing intricate pieces of machinery, but their effective operation is a task just as vital. As one delves into the realm of data pipelines, they are confronted with a trio of formidable technical challenges. Firstly, there is the pivotal issue of how to efficiently populate these pipelines with data. Second, the seamless integration of data pipelines across diverse systems is imperative. Finally, the rapid evolution of the digital landscape demands a sophisticated approach to pipeline management.

Efficient data filling is the keystone to the functionality of data pipelines. Building pipelines, though a complex task in itself, is only the inaugural step. The process of collecting and populating these pipelines is fraught with challenges. Data must be acquired, transformed, and transferred efficiently to ensure a smooth flow. This dynamic process encompasses multiple stages, each with its own unique input-output (IO) profile. The variance in these profiles engenders complexity, which can be likened to a labyrinth within the pipeline. Consequently, data stalls are a constant threat, impeding the fluidity of information flow. These stalls are often precipitated by bottlenecks in the pipeline, which must be identified and rectified to maintain the efficiency of the entire system.

Data Pipelines Are Complex and Require Tuning

The complexity of data pipelines is further exacerbated by the divergence in storage methodologies. Diverse steps of the pipeline require different storage solutions, resulting in a siloed storage environment. These silos are not only cumbersome to manage but also hinder the cross-utilization of data. The inability to harness the full potential of data due to storage isolation is a significant impediment to the seamless operation of pipelines. Overcoming this challenge demands a holistic approach to data management and storage, breaking down silos and fostering a more integrated ecosystem.

In the dynamic realm of data science, the pace of innovation and change is relentless. Science evolves swiftly, and as it does, data infrastructure must keep stride. The challenge here is the glaring incongruence between the speed of traditional infrastructure and the agility required by scientific advancements. Traditional infrastructure, marked by protracted timelines for modification and adaptation, is ill-suited to the rapidity of scientific discovery. In a world where breakthroughs occur in days, infrastructure that takes months or even years to change becomes a critical bottleneck. The evolution of data pipelines must transcend the limitations of slow-moving infrastructure, evolving into agile, adaptive systems capable of accommodating the ever-changing landscape of data science.

Meet the leader behind the success of WEKA

Liran Zvibel, Co-Founded WEKA in 2013. Today, he guides the company’s long-term vision and strategy as its Chief Executive Officer. He previously served as WEKA’s Chief Technology Officer. Before establishing WEKA, Liran co-founded and led research and development at social startup Fusic, where he was responsible for the engineering, design, and development of its rich social media application portfolio. Prior to Fusic, Liran was the principal software architect and a founding member of the technical team at XIV Storage Systems before its acquisition by IBM in 2007; he then managed XIV’s hardware and platform teams at IBM for several years.

Before joining XIV, Liran served as a Software Engineer with the rank of Captain in the Israeli Defense Forces for five years. He earned a Bachelor of Science degree in Mathematics and Computer Science from Tel Aviv University.

“Conquer the Impossible with WEKA”