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Zaloni, Inc.

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  • Turnkey Data Lake: The Fast Path to Business Insights
    Turnkey Data Lake: The Fast Path to Business Insights Nikhil Goel, Lead Architect in Product Management Recorded: Aug 29 2018 37 mins
    Data storage, data compute. Data ingestion. Metadata management. Governance. Visibility. Privacy. Transparency. These are just a few of the considerations you must plan for when modernizing your data platform with a data lake. It can be overwhelming, especially if you try to stitch specialized point products together yourself. Data lake implementations can get out of scope and out of control quickly.

    Why pull your hair out trying to do it yourself? An actionable data lake is within reach. Join us as Nikhil Goel, Zaloni’s Lead Architect in Product Management, discusses the benefits that a turnkey data lake solution can provide as your data grows to meet your organization. Some of the topics covered will be:
    • Storage and compute layers for cloud and on-premises
    • Managed ingestion
    • Zone-based data architecture
    • Self-service access to the data catalog
    • Customer success stories
  • How to Architect a Successful Data Lake
    How to Architect a Successful Data Lake Rajesh Nadipalli, Director of Product Support and Professional Services Recorded: Aug 22 2018 57 mins
    Building a data lake is easy. Architecting a successful data lake that is flexible enough to accept multiple data sources, volumes, and types all while being able to scale with your business is harder.

    Do it wrong and you've created a data swamp. Do it right and you turn data into the most valuable asset in your business.

    Join us and learn from Rajesh Nadipalli, Zaloni’s Director of Product Support and Professional Services, how to:
    - Set your data lake up for success with the right architecture
    - Build guard rails to ensure the accuracy of data in your lake with proper data governance
    - Provide visibility into your lake with a robust data catalog (or tie in with your favorite BI tools)
  • Prepare and Protect GDPR Data using Elastic Stack
    Prepare and Protect GDPR Data using Elastic Stack Sabyasachi Gupta, Lead Software Architect, Zaloni Recorded: Jul 25 2018 49 mins
    The introduction of the European Union’s (EU) General Data Protection Regulation (GDPR) mandates a paradigm change in the way organizations use personal data.

    Elastic Stack is a suite of products which can help with GDPR compliance visibility. X-Pack access control for securing data access, Elasticsearch for indexing data for searches, X-Pack monitoring & alerting for data access events and REST APIs to manage the end to end privacy process.

    Join this session with Sabyasachi Gupta, Lead Software Architect at Zaloni, to learn more about:
    • GDPR primer - what is it and data in scope
    • Different stages of handling GDPR personal data:
    ◦ Prepare
    ◦ Protect
    ◦ Enforce privacy process
    • How Elastic Stack realizes handling the stages of GDPR personal data
  • Next-Level Data Lake Management with the Zaloni Data Platform
    Next-Level Data Lake Management with the Zaloni Data Platform Scott Gidley, VP of Product at Zaloni Recorded: Jun 26 2018 52 mins
    The modern, data-rich enterprise demands access to data at a pace that has outclassed traditional data management platforms. Whether they are utilizing a cloud, hybrid, or on-prem solution, these organizations require capabilities that are vendor-neutral and often implemented with microservices to ensure an agile environment at scale.

    In this webinar, Scott Gidley, Zaloni’s Vice President of Product, will showcase the latest version of the Zaloni Data Platform. This version provides exciting new features to address the growing demands of data-driven companies, including:
    - Managing hybrid and multi-cloud environments
    - Managing your data with zones
    - Cloud-native support
    - Ingestion wizard
    - Platform global search
    - Persona-driven homepage
  • How to Use Microservices to Build a Data Lake on AWS
    How to Use Microservices to Build a Data Lake on AWS Sabyasachi Gupta, Software Architect Recorded: May 30 2018 44 mins
    “Data is the new oil.” Just as we have to drill to get oil, we also need to mine data to get information out of it. Google, Facebook, Netflix and other titans of the digital era use data to build great products that touch every part of human life.

    Regardless of scale, building a managed data lake on AWS requires a robust and scalable technical architecture. They often use microservices during the build process. A microservice architecture is centered around building a suite of small services focused on business capabilities and are independently deployable. It uses lightweight protocols and run on its own processes, which makes a microservice architecture ideal for building decoupled, agile, and automatable data lake applications on AWS.

    Join this session with Sabyasachi Gupta, Software Architect at Zaloni, to learn more about:
    - The what and why of a microservices architecture
    - The different layers of a data lake stack
    - Why is metadata important and how to capture it in AWS
    - The relationship between Serverless and Microservices and available options on AWS
    - How to build a data lake using microservice architecture on AWS
  • Building a Governed Data Lake in the Cloud
    Building a Governed Data Lake in the Cloud Rajesh Nadipalli Recorded: May 23 2018 68 mins
    The three V’s of big data (velocity, volume, variety) continue to grow. There are more data types than ever, arriving faster, in sizes that traditional storage can barely keep up with. This is where transitioning to the cloud makes sense.

    With its on-demand processing, storage scalability, and potential financial savings, the cloud is now a data-oriented organization’s dream. But what model is right for you? What challenges should you look out for? How do you migrate effectively?

    Join Zaloni’s Director of Professional Services and Support, Raj Nadipalli, as he answers these questions - diving into cloud-based data lake use cases, a cloud-based data lake architecture, and more.

    Topics covered include:
    - Benefits of a cloud-based data lake (including hybrid and multi-cloud)
    - Concerns with moving your data lake to the cloud
    - Why metadata matters
    - Cloud use cases
    - A reference architecture
  • Architecting the Cloud: What's Possible Now
    Architecting the Cloud: What's Possible Now Eric Kavanagh & Parth Patel Recorded: Apr 25 2018 19 mins
    All roads lead to cloud: (almost) everyone knows that now. The benefits so outweigh the risks, that even the stodgiest enterprise architects now see the handwriting on the ceiling. While the rules are much different from the days of on-prem software, the reality is that smart cloud architects know their knowledge bar just went higher. How can you stay prepared? Check out this episode of Inside Analysis to hear host Eric Kavanagh interview several experts, including Parth Patel from Zaloni.
  • Agile Data Mastering in the Data Lake
    Agile Data Mastering in the Data Lake Scott Gidley, Vice President of Product Recorded: Apr 11 2018 42 mins
    As new data sources continue to emerge, companies need to create “golden” or master records to achieve a single version of truth, as well as enriched views of customer or product data for applications such as intelligent pricing, personalized marketing, smart alerts, customized recommendations, and more.

    By leveraging machine learning techniques in the data lake, you can integrate data silos and master your data for a fraction of the cost of a traditional master data management solution. Zaloni’s Data Master Extension uses a Spark-based machine learning engine to provide a unique solution for Customer or Product 360° initiatives at the scale of big data.

    In this webinar, Scott Gidley, Zaloni’s Vice President of Product, will lead the discussion around:
    - Using a machine learning approach for matching and linking records
    - Implementing master data management natively in the data lake
    - A practical example of master data in the data lake
  • Zaloni Data Platform: Demo
    Zaloni Data Platform: Demo Scott Gidley, VP of Product at Zaloni Recorded: Apr 2 2018 14 mins
    See what an agile, scalable data lake looks like with the Zaloni Data Platform (ZDP).

    Register to watch this 10-minute demonstration of an end-to-end use case for data management, governance, and self-service data within the ZDP.

    Highlights of this demonstration include:

    - A defined use case for ingesting and transforming sales data of various types and sources.

    - The data's journey through the platform, including ingestion, applying metadata, developing workflows, and exposing the data catalog for self-service preparation.

    - How data is presented to end users and the features that they need to export the data for business analysis.
  • Future-Proof your Data Lake with the Proper Architecture
    Future-Proof your Data Lake with the Proper Architecture Raj Nadipalli, Director of Product Support and Professional Services at Zaloni Recorded: Feb 7 2018 49 mins
    As more and more organizations delve into the world of big data, they’re noticing that it’s not wise to dump data into a data lake without proper guardrails in place. Instead, companies need to architect and build their data lake with scalability, flexibility and governance in mind.

    Based on hundreds of data lake implementations, Zaloni has built a reference architecture that has proven to be scalable and future-proof. This architecture is based on a zone approach through which data can live and travel throughout its lifecycle. This zone-based approach can greatly facilitate data governance and management, particularly if a data lake management platform, such as the Zaloni Data Platform, is in place.

    How should these zones be defined within a data lake environment? What should happen to data within each of these zones? In this webinar, Raj Nadipalli, Director of Product Support and Professional Services at Zaloni, will answer these questions and address how to architect a data lake that is future-proof in the ever-changing big data ecosystem.

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