Igneous Secures $25M in Series C Funding

The round was led by WestRiver Group (WRG), with participation from Madrona Venture Group, NEA, Vulcan Capital and Redpoint Ventures.

igneousIgneous, Inc., a Seattle, WA-based startup delivering an Unstructured Data Management (UDM) as-a-Service solution, raised $25m in Series C funding.

The round was led by WestRiver Group (WRG), with participation from Madrona Venture Group, NEA, Vulcan Capital and Redpoint Ventures.

The company intends to use the funds to expand technology and go-to-market investments.

Led by Kiran Bhageshpur, CEO, Igneous provides an Unstructured Data Management (UDM) as-a-Service solution that allows organizations to see, organize, protect and mobilize their most valuable data assets.

Its API-enabled, cloud-native platform combines all UDM functions so that organizations can tap the value of their unstructured data, while reducing risk and optimizing IT resource utilization.

FinSMEs

18/03/2019

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Data Lakes Market 2023 by Emerging Key Players, Top Countries, Types, Applications, and …

Data Lakes market report will help you to know each and every fact of … United States believe that big data analytics offers a substantial competitive …

Data Lakes

Data Lakes Market report provides emerging market drivers, challenges, opportunities for Data Lakes Industry. It focuses on the latest trends and recent developments of Data Lakes Industry. Data Lakes market report will help you to know each and every fact of keyword industry. Data Lakes market also covers growth potential, market size, demand by buyer and suppliers and forecast details.

Data Lakes Market is expected to grow at a CAGR of 27.4% during the forecast period.

Get Sample PDF of Data Lakes Market Report @ https://www.absolutereports.com/enquiry/request-sample/13104076

Global Data Lakes Market was valued at USD 3.24 billion in 2017, and is expected to reach a value of USD 14.01 billion by 2023 at a CAGR of 27.4%, over the forecast period (2018-2023). The scope of the report is limited to deployment type which include Cloud, On-premise. The End Users considered in the scope of the report include BFSI, Retail, Entertainment and Media, Healthcare, IT and Telecommunications and Manufacturing.

Data Lakes have become an economical option for many companies rather than an option for data warehousing. Data warehousing involves additional computing of data before entering the warehouse, unlike Data Lakes. The cost of maintaining a data Lake is lower than maintaining a data Lake owing to the number of operations involved in building the database for warehouses. The speed of data retrieval is also better for data lakes compared to data warehouses which have proved them to be a viable option compared to warehouses. According to O’Reilly Data Scientist Salary Survey, it has been identified that about one-third of the data scientists spend time for doing basic operations such as basic extraction/transformation/load (ETL), data cleaning, and basic data exploration rather than true analytics or data modeling which reduces the efficiency of the process. The growing use of IoT in many offices and informal spaces has further emphasized in the need for data lakes for quicker and efficient manipulation of data.

Need For Increasing Agility And Accessibility Of Businesses.

According to IBM most of the companies in the United States have about 100 Terabytes of data stored. Companies have been struggling to manage such data as increasing the storage capacity, and processing power of the existing systems involves in high cost and not a sustainable option. Data lakes have emerged as a practical solution to exponentially increasing data. The variety of data emerging in the present day scenario has been broader in range as many developments such as modern cars alone have about 100 sensors per car, NSE has nearly 1TB of trade information per session, according to IBM. Owing to such developments the use of data lakes aids in improving the agility of organizations such as analytics firms to retrieve, store and manage data in a better manner.

Banking Is Expected To Be One of the Primary Recipients of the Technology

Banks have been increasing the use of data lakes to integrate data across various domains to create a central database. Australia and New Zealand Banking Group (ANZ) has been implementing a project to aggregate all the data ponds across its domains to create a central data lake for the bank which will allow the bank to shift from the typically used data warehouse architecture. In the present scenario, the implementation of data lakes in the domain has not been effective as many data lakes have been changing into data swamps. Customers are unable to access data with ease, and data lakes have become the bottlenecks for organizations. Banks have been investing in data engineers to provide more responsive data lakes to tackle with consumer requirements. Banks have been trying to increase the utility of data for on the go solutions. State Bank of India (SBI) has been providing data lakes, apart from the typically used data warehouse, to bank executives, deputy managing director and chief information to deliver on the go analytics.

North America Is Expected To Have High Adoption for Data Lakes Market

According to Capgemini, more than 60% of the financial institutions in the United States believe that big data analytics offers a substantial competitive advantage over the competitors and more than 90% of the companies believe that the big data initiatives determine the chance for success in the future. Data Lakes are needed for the use of Smart meter applications. In Canada, BC Hydro uses an EMC data lake for analyzing data aggregated by various smart meters. The data then enables in detecting discrepancies in the system. This has aided in achieving savings of 75% of the electricity due to theft. The number of Smart Meters in the region have also been growing in usage. Owing to increase in the usage of smart meters a huge amount of data is being generated which needs the use of Data Lakes. In the United States, a total of 70,823,466 smart meters have been installed according to U.S Energy Information Administration.

Key Developments in Data Lakes Market

• November 2017: Sciformix Corporation announced its adoption of Oracle Argus Enterprise Edition platform to offer its customers in the life science industry. Through its collaboration with Oracle, Sciformix can offer its customers a fully automated and integrated safety database solution, allowing easy reporting and analytics for improving the quality and efficiency of drug safety operations.

Major Players: MICROSOFT CORPORATION, AMAZON.COM INC., CAPGEMINI SE, ORACLE CORPORATION, TERADATA CORPORATION, SAP SE, IBM CORPORATION, SOLIX TECHNOLOGIES INC., INFORMATICA CORPORATION, DELL EMC, ENTERPRISE DATA LAKES, HITACHI DATA SYSTEMS, and CAZENA INC, amongst others.

Inquire Data Lakes Market Report @ https://www.absolutereports.com/enquiry/pre-order-enquiry/13104076

Global Data Lakes Market: Regional Segment Analysis (Regional Production Volume, Consumption Volume, Revenue and Growth Rate 2013-2023):

  • North America: United States, Canada and Mexico
  • Europe: Germany, UK, France, Italy, Russia, Spain and Benelux
  • Asia Pacific: China, Japan, India, Southeast Asia and Australia
  • Latin America: Brazil, Argentina and Colombia
  • Middle East and Africa: Saudi Arabia, UAE, Egypt, Nigeria and South Africa

Data Lakes Market Covers Following Points in TOC:

Chapter 1: Data Lakes Market Definition

Chapter 2: Research Methodology of Data Lakes Market

Chapter 3: Data Lakes Market Executive Summary

Chapter 4: Data Lakes Market Overview Includes Current Market Scenario, Porter’s Five Forces Analysis, Bargaining Power of Suppliers and Consumers, Threat of New Entrants and Substitute Product and Services

Chapter 5: Market Dynamics Covers Drivers, Restraints, Opportunities and Challenges

Chapter 6: Data Lakes Market Segmentation by Types, End-User, and Applications Forecast to 2023

Chapter 7: Data Lakes Market Segmentation by Geographical Regions

Chapter 8: Competitive Landscape of Data Lakes Market Includes Mergers & Acquisition Analysis, Agreements, Collaborations, and Partnerships, New Products Launches

Chapter 9: Key Players for Data Lakes Market

Price of Report: $ 4250 (SUL)

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Absolute Reports is an upscale platform to help key personnel in the business world in strategizing and taking visionary decisions based on facts and figures derived from in-depth market research. We are one of the top report resellers in the market, dedicated to bringing you an ingenious concoction of data parameters.

Contact Us:

Name: Ajay More

Organization: Absolute Reports

Phone: +44 20 3239 8187 / +14242530807

Email: [email protected]

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Upcoming Trends of Vehicle Analytics Market 2023 by Estimated Growth Rate, Revenue, Price …

With vehicles nowadays generating gigabytes of data in moments, the … Also, data analytics will allow the vehicles to interact, navigate, collaborate without … which limits the fallout from large-scale recalls, minimizing unnecessary …

Vehicle Analytics

Vehicle Analytics Market report provides emerging market drivers, challenges, opportunities for Vehicle Analytics Industry. It focuses on the latest trends and recent developments of Vehicle Analytics Industry. Vehicle Analytics market report will help you to know each and every fact of keyword industry. Vehicle Analytics market also covers growth potential, market size, demand by buyer and suppliers and forecast details.

Vehicle Analytics Market is expected to grow at a CAGR of 24.2% during the forecast period.

Get Sample PDF of Vehicle Analytics Market Report @ https://www.absolutereports.com/enquiry/request-sample/13104020

The Vehicle Analytics market was valued at USD 1.12 billion in 2017 and is expected to grow at a CAGR of 24.2% during the forecast period (2018 – 2023), to reach USD 4.14 billion by 2023. Vehicle analytics has applications such as in predictive maintenance, safety and security management, driver performance analysis, amongst various others. The end-users of this technology are OEM’s, regulatory bodies, insurers, amongst various others. The scope of our study is geographically limited to North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

Automobiles are being transformed by technologies, applications, and services through the adoption of various things such as sensors, artificial intelligence, and big data analysis. With vehicles nowadays generating gigabytes of data in moments, the opportunity to deliver exceptional customer experiences and business process is more significant than ever. Apart from this, growth in the connected car industry is expected to provide a significant number of challenges as well as opportunities to the automotive sector, including analytics. Cars of the future cars are poised to show immense intelligence with prodigious connectivity. Also, data analytics will allow the vehicles to interact, navigate, collaborate without human mediation, and create a vast volume of data. Apart from this, use of vehicle telematics, and advancements in machine learning and AI are expected to fuel the market growth. However, the high cost of this technology can limit the penetration to high-end luxury cars and is expected to challenge the vehicle analytics market.

Rising Demand for Predictive Maintenance to Augment the Market

As automakers are constantly assessing the performance of the vehicle part in real time through sensors, this unlocks the opportunity towards a predictive maintenance approach. Using predictive maintenance, data can be pulled out from vehicles of a given year and model and that information can be compared with warranty repair trends. These trending issues are carefully observed and addressed, which limits the fallout from large-scale recalls, minimizing unnecessary wrench time, and potentially saving lives in the process. Access to massive datasets adds further value to predictive analytics as automotive companies will be able to help connected vehicle to spend more time on the road and less time in the shop. In addition, the vehicle analytics market is still an open place for advanced predictive solutions as it is still dominated by simplistic predictive maintenance solutions, which monitors wear and usage of clutches, brake pads and similar wear-out apparatus is monitored and projections are made about the future.

Asia-Pacific Expected to Witness the Fastest Growth

The region is witnessing a growing dominance of connected and autonomous vehicle. Also, an increasing penetration of new technology companies making ways into the automotive industry is expected to lead to a new era of automotive analytics. China’s ambition to have at least 30 million autonomous vehicles within a decade (2018-2028) is expected to drive the demand for automobile analytics. Apart from the taxis, Nissan and automakers in the country have an aim to bring semiautonomous vehicles to city streets by 2020, the year in which the Summer Olympics will take place in Tokyo. However, countries such as India, where the government has completely rejected the idea of autonomous vehicles on its roads, the vehicle analytics market is expected to be driven by adoption in high end cars.

Key Developments in Vehicle Analytics Market

• February 2018 – At the Mobile World Congress, SAP SE expanded the SAP Vehicles Network initiative to add HERE Technologies, MasterCard and Postmates. Together, the Network aims to work on various new features of vehicle analytics.

• October 2017 – Teletrac Navman announced a collaboration with Noregon. This partnership aims to combine the fleet management capabilities of Teletrac Navman and the vehicle health and safety diagnostics of Noregon, to provide a holistic view of vehicle fitness in real time.

Major Players: SAP SE, CLOUDMADE, GENETEC INC. , HARMAN INTERNATIONAL,IBM CORPORATION, INQUIRON LTD.,INTELLIGENT MECHATRONIC SYSTEMS INC., MICROSOFT CORPORATION, and TELETRAC NAVMAN US LTD., amongst others.

Inquire Vehicle Analytics Market Report @ https://www.absolutereports.com/enquiry/pre-order-enquiry/13104020

Global Vehicle Analytics Market: Regional Segment Analysis (Regional Production Volume, Consumption Volume, Revenue and Growth Rate 2013-2023):

  • North America: United States, Canada and Mexico
  • Europe: Germany, UK, France, Italy, Russia, Spain and Benelux
  • Asia Pacific: China, Japan, India, Southeast Asia and Australia
  • Latin America: Brazil, Argentina and Colombia
  • Middle East and Africa: Saudi Arabia, UAE, Egypt, Nigeria and South Africa

Vehicle Analytics Market Covers Following Points in TOC:

Chapter 1: Vehicle Analytics Market Definition

Chapter 2: Research Methodology of Vehicle Analytics Market

Chapter 3: Vehicle Analytics Market Executive Summary

Chapter 4: Vehicle Analytics Market Overview Includes Current Market Scenario, Porter’s Five Forces Analysis, Bargaining Power of Suppliers and Consumers, Threat of New Entrants and Substitute Product and Services

Chapter 5: Market Dynamics Covers Drivers, Restraints, Opportunities and Challenges

Chapter 6: Vehicle Analytics Market Segmentation by Types, End-User, and Applications Forecast to 2023

Chapter 7: Vehicle Analytics Market Segmentation by Geographical Regions

Chapter 8: Competitive Landscape of Vehicle Analytics Market Includes Mergers & Acquisition Analysis, Agreements, Collaborations, and Partnerships, New Products Launches

Chapter 9: Key Players for Vehicle Analytics Market

Price of Report: $ 4250 (SUL)

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About Absolute Reports:

Absolute Reports is an upscale platform to help key personnel in the business world in strategizing and taking visionary decisions based on facts and figures derived from in-depth market research. We are one of the top report resellers in the market, dedicated to bringing you an ingenious concoction of data parameters.

Contact Us:

Name: Ajay More

Organization: Absolute Reports

Phone: +44 20 3239 8187 / +14242530807

Email: [email protected]

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Upcoming Trends of Data Preparation Market 2023 by Estimated Growth Rate, Revenue, Price …

Data Preparation Market report provides emerging market drivers, challenges, opportunities for Data Preparation Industry. It focuses on the latest …

Data Preparation

Data Preparation Market report provides emerging market drivers, challenges, opportunities for Data Preparation Industry. It focuses on the latest trends and recent developments of Data Preparation Industry. Data Preparation market report will help you to know each and every fact of keyword industry. Data Preparation market also covers growth potential, market size, demand by buyer and suppliers and forecast details.

Data Preparation Market is expected to grow at a CAGR of 22.7% during the forecast period.

Get Sample PDF of Data Preparation Market Report @ https://www.absolutereports.com/enquiry/request-sample/13104069

Data Preparation Market has been valued at USD 1.78 billion in 2017 and is expected to grow at a CAGR of 22.7% during the forecast period (2018 – 2023), to reach USD 6.06 billion by 2023. The scope of the report is limited to deployment type including on-premise and cloud, enterprise size including SMEs and mid-sized and large enterprise, and end user vertical including BSFI, Healthcare, retail, manufacturing, IT and telecommunication, and others. The regions considered in the scope of the report include North America, Europe, Asia Pacific, Latin America, and Middle East and Africa. The study also emphasizes the benefits of semiconductor memory in accordance with diverse application and future prospect of the same.

The companies are demanding fast debugging time to generate meaningful insights than ever to sustain in the market owing to digital disruption. As a result, the requirement for analytics, particularly data analytics is becoming pervasive across various size of organizations. The data analytics professionals and companies are finding difficulties in driving insights owing to rising complexities in the procurement of data. The data collected are usually unstructured or semi-structured which requires being in the uniform format leading to maximum yield and efficiency. In response to this problem, companies are devoting more or maximum time in data preparation to generate and have clean data to perform analytics. According to Oracle, it is estimated that approximately 90% of the time is invested for data preparation in the chain of data analytics.

Rising Complexity of Data Structures

The increasing volume of unstructured and semi-structured data are increasing owing to diverse data sources which require data preparation as a vital process which is likely to foster the market growth. Additionally, the complex data is required to have precise data cleaning to extract meaningful insights from the same which is expected to propel the market growth over the next six years.

Cloud to Exhibit the Highest Growth

The deployment of data preparation tools over cloud offers various advantages over on-premise including scalability and flexibility. Companies such as Google are moving towards the adoption of cloud-based tools owing to enhance the flexibility of the same. In September 2017, Google introduced a beta version of public cloud data prep which is likely to compel similar industry players to enter in the market which is, in turn, expected to fuel the market growth over the forecast period.

Asia Pacific to Witness the Highest Growth

There is a rise in crimes reported in the BFSI sector in India, leading to financial sectors rapidly adopting fraud detection techniques to prevent the same. These techniques predict the fraud(s) based on the big data generated from users and previous data which requires intense amount of data preparation thereby having a positive impact on the market in Asia Pacific region.

Key Developments in Data Preparation Market

• January 2018 – IBM launched platform approach to data science offering such as hybrid data management, unified integration & governance platform, and data science & business analytics platform which will comprise data cleaning as a crucial step thereby widening the consumer base

Major Players: MICROSOFT CORPORATION, TABLEAU SOFTWARE, INC., QLIK TECHNOLOGIES INC., SAP SE, IBM, SAS INSTITUTE, ALTERYX, INC, CAMBRIDGE SEMANTICS, and TALEND, amongst others.

Inquire Data Preparation Market Report @ https://www.absolutereports.com/enquiry/pre-order-enquiry/13104069

Global Data Preparation Market: Regional Segment Analysis (Regional Production Volume, Consumption Volume, Revenue and Growth Rate 2013-2023):

  • North America: United States, Canada and Mexico
  • Europe: Germany, UK, France, Italy, Russia, Spain and Benelux
  • Asia Pacific: China, Japan, India, Southeast Asia and Australia
  • Latin America: Brazil, Argentina and Colombia
  • Middle East and Africa: Saudi Arabia, UAE, Egypt, Nigeria and South Africa

Data Preparation Market Covers Following Points in TOC:

Chapter 1: Data Preparation Market Definition

Chapter 2: Research Methodology of Data Preparation Market

Chapter 3: Data Preparation Market Executive Summary

Chapter 4: Data Preparation Market Overview Includes Current Market Scenario, Porter’s Five Forces Analysis, Bargaining Power of Suppliers and Consumers, Threat of New Entrants and Substitute Product and Services

Chapter 5: Market Dynamics Covers Drivers, Restraints, Opportunities and Challenges

Chapter 6: Data Preparation Market Segmentation by Types, End-User, and Applications Forecast to 2023

Chapter 7: Data Preparation Market Segmentation by Geographical Regions

Chapter 8: Competitive Landscape of Data Preparation Market Includes Mergers & Acquisition Analysis, Agreements, Collaborations, and Partnerships, New Products Launches

Chapter 9: Key Players for Data Preparation Market

Price of Report: $ 4250 (SUL)

Purchase Data Preparation Market Report @ https://www.absolutereports.com/purchase/13104069

About Absolute Reports:

Absolute Reports is an upscale platform to help key personnel in the business world in strategizing and taking visionary decisions based on facts and figures derived from in-depth market research. We are one of the top report resellers in the market, dedicated to bringing you an ingenious concoction of data parameters.

Contact Us:

Name: Ajay More

Organization: Absolute Reports

Phone: +44 20 3239 8187 / +14242530807

Email: [email protected]

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Can Data Lakes Solve Machine Learning Workload Challenges?

One can store data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data …
W3Schools


Year after year, the field of ML is progressing at break-neck speed, and new algorithms and techniques are entering the space at a high frequency. Also, machine learning workloads are becoming increasingly more prevalent. However, there are significant challenges in democratizing machine learning and reliably scaling and deploying ML workloads.

In this article, we will have a look at some of the ML workload challenges and how data lakes can help overcome them.

Challenges In ML Workloads

Data Collection

ML workloads typically benefit from data — the more data is put into these workloads the better they become. So in order to make the most of the ML workloads, organisations across the world are looking for ways to collect data. However, the cost data collection and storage has to be low — one just cannot spend a huge amount of money collecting and storing data durably as one would not know when are where the data would be used.


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Extremely Experimental

ML workloads are iterative and experimental — it takes multiple experiments to check how the models are working. So, it is quite challenging. To over this ML workload challenge, a disposable infrastructure is something that organisations need. Why? Because this kind of infrastructure will allow training the ML model and when it’s no longer needed it can be disposed of.

Another thing that organisations working in the field of Machine Learning should keep in mind that they should be able to decouple compute and storage in order to run the workloads only when we need them.

Data Exploration

It is another challenge that organisations face. Collecting and storing huge amount of data is one thing, however, the struggle that organisations have to go through is exploring that data — what’s the format, what’s the schema, what data is usable, and what’s the data source.

It’s a whole different process and takes a lot of work. Talking about the exploration of data, schema on read is something that every organisation leverage. If you don’t know schema on read, it a data analysis strategy. In schema on read, data is applied to a plan or schema as it is pulled out of a stored location, rather than as it goes in. Another important thing to keep in mind is a data catalogue that centralizes all information on the data in one location.

Flexibility In Tool Set Selection

Selecting the set of tools is another challenge — tool sets differ based on the developer. Two different developers might not use the same kind of tool. So, it is important to have flexibility in selecting the correct set of tools. One should be able to quickly plug and play different tools and frameworks as there are a lot of new technologies are entering the space. Another thing is to keep data in the open data format as that it goes really well with most of the open source engines.

A Solution To All The Pain Points: Data Lake

A Data Lake is a central location in which to store all your data, regardless of its source or format. One can store data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Over the years, the concept of data lake has gained a lot of traction and now, in order to successfully generate business value from data and outperform peers, organisations across the world are actively working on building data lakes.

We have already mentioned the challenges that organisations face while working with ML workloads, and as to solve the pain points, building a data lake is a great option as it solves the issues.

  • Data Lakes let you import any amount of data that can come in real-time.
  • Data Lakes allow you to store non-relational and relational data from IoT devices, web sites, mobile apps, social media, and corporate applications
  • Written at the time of analysis (schema-on-read)
  • Faster query results and low-cost storage
  • Data Lakes allow various roles in your organization like data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks.

The ability to a data lake to harness more data, from different sources, in less time, is what makes it a better option when dealing with ML workloads. It not only empowers users to collaborate and analyze data in different ways but also helps in making decisions faster.


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