BIG DATA MANAGEMENT

Synopsys Launches the Era of Smarter SoC Design with ML-Driven Big Data Analytics Technology

Synopsys | June 03, 2022

Synopsys
Driving greater design productivity by harnessing previously untapped design insights with machine learning technology, Synopsys, Inc. (Nasdaq: SNPS) today announced a critical expansion of its EDA data analytics portfolio with the introduction of Synopsys DesignDash design optimization solution. As a complementary product to Synopsys' market-leading Digital Design Family and Synopsys DSO.ai™, the award-winning AI-driven design-space-optimization solution, Synopsys DesignDash is a comprehensive data-visibility and machine intelligence-guided design optimization solution that enables unmatched productivity in advanced SoC design. The Synopsys DesignDash solution delivers a real-time, unified, 360-degree view of all design activities for faster decision making, a deeper understanding of run-to-run, design-to-design and project-to-project trends, and enhanced collaboration in the SoC development process.

"As a leading supplier of SoCs that are powering and transforming numerous high-impact industries, we pride ourselves on being able to push the limits of achievable device performance while also accelerating our customers' time-to-market," said Hiroshi Ikeda, director, Methodology Development Office, Global Development Group at Socionext. "We're very excited by the Synopsys DesignDash analytics solution as a systematic way to capture, consume and evaluate our vast design activity in a scalable way, enabling us to share and transfer expert knowledge across our worldwide design teams to enhance productivity and efficiency."

Unlocking the Potential Within Vast Volumes of Digital Design Data

The digital design flow holds a wealth of information from myriad sources that, properly utilized, could help teams optimize increasingly complex designs faster. According to Gartner® Inc., "By 2023, overall analytics adoption will increase from 35% to 50%, driven by vertical- and domain-specific augmented analytics solutions."1.

The introduction of Synopsys DesignDash is the latest step in a multi-year, multi-disciplinary development effort to address the need for exponential gains in design productivity in the face of massive growth in system complexity, shrinking market windows and an increasingly challenging resource landscape.

The cloud-optimized Synopsys DesignDash design optimization solution greatly enhances design productivity by:

  • Providing extensive real-time design status through powerful visualizations and interactive dashboards.
  • Deploying deep analytics and machine learning to extract and reveal actionable understanding from vast volumes of structured and unstructured EDA metrics and tool-flow data.
  • Quickly classifying design trends, identifying design limitations, providing guided root-cause analysis and delivering flow consumable, prescriptive resolutions.

With deeper design insights, designers can achieve more effective debug and optimization workflows, realize improved quality of results (QoR) and significantly extend overall design- and project-flow efficiency and effectiveness. This extensive insight and real-time visibility additionally deliver comprehensive resource monitoring and tracking that spans all design activities, enabling more data-driven management and risk mitigation throughout the design process. Synopsys DesignDash is natively integrated with the Synopsys Digital Design family of products for seamless data capture, resulting in insights that further accelerate the path towards design closure. The solution complements the Synopsys SiliconDash product, part of the Synopsys Silicon Lifecycle Management Family, forming a pre-silicon to post-silicon data continuum, maximizing opportunities for valuable data analysis across the complete design-to-silicon lifecycle.

"SoC complexity across all application niches continues to rise as more functionality and performance is required. "Through the data analytics and machine learning capabilities of the Synopsys DesignDash technology, engineering teams now have an efficient way to share and utilize valuable insights that would otherwise take hours of manual work to compile or, in some cases, not be accessible."

Karl Freund, founder, and principal analyst at Cambrian-AI Research

"The semiconductor industry needs a dramatic improvement in design process productivity. Improving the quality and speed of engineering decisions with a comprehensive EDA data analytics platform is a critical step in this direction," said Sanjay Bali, vice president of Marketing and Strategy for the Silicon Realization Group at Synopsys. "Synopsys DesignDash unlocks the potential of the significant and growing volumes of EDA metrics and design-flow data, heralding a new era in smarter IC design by deploying an expanse of advanced data analytics and targeted machine learning to effectively guide design teams to achieve or exceed their product goals and schedules."

About Synopsys
Synopsys, Inc. is the Silicon to Software™ partner for innovative companies developing the electronic products and software applications we rely on every day. As an S&P 500 company, Synopsys has a long history of being a global leader in electronic design automation (EDA) and semiconductor IP and offers the industry's broadest portfolio of application security testing tools and services. Whether you're a system-on-chip (SoC) designer creating advanced semiconductors, or a software developer writing more secure, high-quality code, Synopsys has the solutions needed to deliver innovative products.

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Saturn Cloud and Bodo.ai Partner to Bring Extreme Performance Python to Data Scientists

Saturn Cloud | June 02, 2022

Saturn Cloud, the data science and machine learning platform and bodo.ai, a parallel data compute platform providing extreme scale and speed for Python, have announced their partnership to take Python analytics performance to the next level for data science teams. Data scientists develop multiple workflows across teams, and rely on Saturn Cloud to provide a collaborative environment and computing resources. With this partnership, those teams now have seamless access to the Bodo platform - allowing them to scale prototypes to petabyte-scale parallel-processing production without any tuning or re-coding. Saturn Cloud's pre-built tools allow data science teams to collaborate and scale easily, without locking users into patterns. Instead, the platform encourages the workflow the user already has, while providing an environment where they don't need to rely on dev sources or manage compute environments. It prioritizes keeping the data scientist self-sufficient, while being able to collaborate and share work more efficiently. Bodo offers a parallel compute platform providing extreme scale and speed, but with the simplicity and flexibility of using native Python. In contrast to using libraries and frameworks like Spark, Bodo is a new type of compiler offering automatic parallelism and high efficiency surpassing 10,000+ cores. Bodo can also be used natively with analytics packages such as Pandas, NumPy, SciKit Learn, and more. The joint solution is available immediately, with bodo.ai software running within Saturn Cloud resources. Saturn Cloud provides a pre-built template with Bodo already installed and configured. Then, users are able to access the functionality of bodo.ai within JupyterLab or via SSH from VSCode, PyCharm, or the terminal. By using Saturn Cloud, users are able to get up to 4TB of RAM and 128 vCPUs, all backing the powerful software of Bodo. You can try the following examples right away here: Use Bodo to speed up feature engineering and model training or use Bodo to speed up data manipulation and analysis. "Our partnership is focused on providing massive speed and productivity improvements to data scientists struggling with large-scale analytics projects. Bodo's platform adds terabyte-scale processing with unheard-of infrastructure efficiencies for Saturn Cloud users." Behzad Nasre, CEO, Bodo "We not only want to provide a flexible workspace for data science teams, but enable greater Python scaling capabilities to increase productivity in projects that are more demanding. This joint offering with Bodo will give users an opportunity to take their work to the next level with automatic parallelization for better overall performance," says Sebastian Metti, one of the Saturn Cloud founders. About Saturn Cloud Saturn Cloud is a data science and machine learning platform flexible enough for any team. Collaborate together in the cloud on analyses and model training, then deploy your code. All using the same patterns you're used to, but with cloud scale. Learn more here. About Bodo Founded in 2019, Bodo.ai is an extreme-performance parallel compute platform for data analytics, scaling past 10,000 cores and petabytes of data with unprecedented efficiency and linear scaling. Leveraging automatic parallelization and the first inferential compiler, Bodo is helping F500 customers solve some of the world's largest data analysis problems. And doing so in a fraction of traditional time, complexity, and cost, all while leveraging the simplicity and flexibility of native Python. Developers can deploy Bodo on any infrastructure, from a laptop to a public cloud.

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Datafold Launches Open Source data-diff to Compare Tables of Any Size Across Databases

Datafold | June 23, 2022

Datafold, a data reliability company, today announced data-diff, a new open source cross-database diffing package. This new product is an open source extension to Datafold’s original Data Diff tool for comparing data sets. Open source data-diff validates the consistency of data across databases using high-performance algorithms. In the modern data stack, companies extract data from sources, load that data into a warehouse, and transform that data so that it can be used for analysis, activation, or data science use cases. Datafold has been focused on automated testing during the transformation step with Data Diff, ensuring that any change made to a data model does not break a dashboard or cause a predictive algorithm to have the wrong data. With the launch of open source data-diff, Datafold can now help with the extract and load part of the process. Open source data-diff verifies that the data that has been loaded matches the source of that data where it was extracted. All parts of the data stack need testing for data engineers to create reliable data products, and Datafold now gives them coverage throughout the extract, load, transform (ELT) process. “data-diff fulfills a need that wasn’t previously being met. “Every data-savvy business today replicates data between databases in some way, for example, to integrate all available data in a warehouse or data lake to leverage it for analytics and machine learning. Replicating data at scale is a complex and often error-prone process, and although multiple vendors and open source tools provide replication solutions, there was no tooling to validate the correctness of such replication. As a result, engineering teams resorted to manual one-off checks and tedious investigations of discrepancies, and data consumers couldn’t fully trust the data replicated from other systems. Gleb Mezhanskiy, Datafold founder and CEO Mezhanskiy continued, “data-diff solves this problem elegantly by providing an easy way to validate consistency of data sets across databases at scale. It relies on state-of-the art algorithms to achieve incredible speed: e.g., comparing one-billion-row data sets across different databases takes less than five minutes on a regular laptop. And, as an open source tool, it can be easily embedded into existing workflows and systems.” Answering an Important Need Today’s organizations are using data replication to consolidate information from multiple sources into data warehouses or data lakes for analytics. They’re integrating operational systems with real-time data pipelines, consolidating data for search, and migrating data from legacy systems to modern databases. Thanks to amazing tools like Fivetran, Airbyte and Stitch, it’s easier than ever to sync data across multiple systems and applications. Most data synchronization scenarios call for 100% guaranteed data integrity, yet the practical reality is that in any interconnected system, records are sometimes lost due to dropped packets, general replication issues, or configuration errors. To ensure data integrity, it’s necessary to perform validation checks using a data diff tool. Datafold’s approach constitutes a significant step forward for developers and data analysts who wish to compare multiple databases rapidly and efficiently, without building a makeshift diff tool themselves. Currently, data engineers use multiple comparison methods, ranging from simple row counts to comprehensive row-level analysis. The former is fast but not comprehensive, whereas the latter approach is slow but guarantees complete validation. Open source data-diff is fast and provides complete validation. Open Source data-diff for Building and Managing Data Quality Available today, data-diff uses checksums to verify 100% consistency between two different data sources quickly and efficiently. This method allows for a row-level comparison of 100 million records to be done in just a few seconds, without sacrificing the granularity of the resulting comparison. Datafold has released data-diff under the MIT license. Currently, the software includes connectors for Postgres, MySQL, Snowflake, BigQuery, Redshift, Presto and Oracle. Datafold plans to invite contributors to build connectors for additional data sources and for specific business applications. About Datafold Datafold is a data reliability platform that helps data teams deliver reliable data products faster. It has a unique ability to identify, prioritize and investigate data quality issues proactively before they affect production. Founded in 2020 by veteran data engineers, Datafold has raised $22 million from investors including NEA, Amplify Partners, and YCombinator. Customers include Thumbtack, Patreon, Truebill, Faire, and Dutchie.

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IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

IBM | July 07, 2022

IBM today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time. Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020. As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1 Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems. Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription. The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations. For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation. "Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale." Daniel Hernandez, General Manager for Data and AI, IBM Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides. "You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients." Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022. About Databand.ai Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. About IBM IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service.

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Katipult Launches Enterprise-Grade Data Integration Capabilities to its DealFlow Platform

Katipult | July 06, 2022

Katipult Technology Corp. , a leading Fintech provider of software for powering the exchange of capital in equity and debt markets, announced today that its private placements platform, DealFlow, has been upgraded with the addition of a new enterprise-grade data integration module – DealFlow: DataHub. This module enables users to securely link their backend systems with the DealFlow platform, allowing them to directly populate subscription documents with the latest information from their systems of record. "We're very excited to announce the launch of the DealFlow: DataHub module. Our experience working with investment banks and broker dealers showed us that being able to seamlessly interface with their legacy systems of record is critical for helping them accelerate the pace of digital transformation. DealFlow:DataHub further amplifies the efficiency-boosting capabilities of DealFlow by removing yet another manual step in the private placements process. Not only is scalability improved, but there are also positive knock-on effects on compliance as data integrity and continuity are preserved." Gord Breese, Katipult CEO DealFlow:'s DataHub extracts large volumes of data from the commonly used systems of record in the industry, such as ISM or Dataphile. The data is then streamlined and used to populate the intelligent digital subscription documents that are core to the DealFlow platform. With the addition of DealFlow: DataHub, customers will no longer need to manually input or update the data that will populate the subscription documents. Further, DataHub will also enable single sign-on to the DealFlow platform, allowing users to sign on with their standard enterprise credentials. Katipult's goal with DealFlow is to help institutions unlock the full potential of private placements by streamlining as many processes as possible. DealFlow: DataHub represents yet another step forward in that direction. About Katipult Katipult is a provider of industry leading and award-winning software infrastructure for powering the exchange of capital in equity and debt markets. Our cloud-based platform and solutions digitize investment workflow by eliminating transaction redundancy, strengthening compliance, delighting investors, and accelerating deal flow. Katipult provides unparalleled adaptability for regulatory compliance, asset structure, business model, and localization requirements.

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