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7 Things to Know About Low-Code Development Platforms

Posted in: development, IT Strategy, Leadership, low-code, low-code platforms - Aug 04, 2021

What is one thing to know about low-code development platforms?

To help business owners fully understand low-code development, we asked low-code developers and business professionals to share their best insights on this question. From double-checking effects on page speed to being more user-friendly, there are several things to keep in mind about low-code development platforms for the growth of your business.

Here are seven things to know about low-code development platforms:

Verify Customization Possibilities

As a low-code development platform, Comidor offers truly custom applications and integration. We are professional developers who write thousands of lines of code, so we can offer our clients low-code tools that require no development skills.

Our low-code programming is enabled with workflow automation, Robotic Process Automation, Artificial Intelligence and Machine Learning. All these features come together to help our clients utilize Comidor‘s low-code platform for easier low-code app development and cost reduction.

Spiros Skolarikis, Comidor

Not All Applications Are the Same

The main thing to know about low-code development platforms is that they are not all the same. The term “low-code” covers a wide range of applications and products, from simple templates that allow users to create forms or reports with little or no coding, to full-fledged development environments with visual programming languages and tools to develop fully functional applications.

If you are used to one platform, you won’t automatically find your way around another. Much comes down to experience and trial and error.

Peter Thaleikis, Developer

Possess a Variety of Uses

One thing to know about low-code development platforms is that they’re great for marketing. As a content marketer, I try to stay up-to-date with marketing trends, and low-code is something marketers can take advantage of. Low-code allows marketing teams to put together applications without using valuable IT resources.

Using a low-code content management system with a fresher content architecture simplifies the idea-to-deployment process. Last but not least, low-code platforms allow for more automation and increased productivity when it comes to marketing. At first glance, low-code doesn’t seem like a marketing tool, but when you think about it, it really is.

Francesca Nicasio, Payment Depot

Low-Code Development Is More User-Friendly

Low-code, in my opinion, is software development using a visual drag-and-drop interface. The concept is basic, and the user interface is intuitive. Yet the outcomes are scalable, safe, and fast. Lowering the quantity of “hand coding” (creating code from scratch) and increasing the amount of code reuse and app development are the key goals of low-code development.

When you use a visual IDE to build a component, it’s straightforward to reuse that component in multiple contexts. You benefit from the initial speed of an IDE and then gain even greater speed by reusing the components created.

Eric Carrell, SurfShark

Developers Are Still Needed

A common misconception around low-code development platforms is that developers are no longer needed. Low-code platforms don’t replace developers. They simply can help drive higher productivity from developers by allowing them to deliver more deployments faster, and with less effort.

Before you begin calculating the cost savings of reducing the number of software engineers on staff, understand that low-code platforms aren’t a replacement. They’re just there to support.

Brett Farmiloe, Markitors

Some Basic Coding Skills Are Still Required

Low-code is not the same as no-code. While both provide visual modeling, no-code relies entirely on a visual user interface with no code writing necessary. And while low-code’s drag-and-drop features reduce the steps in the development process, it does involve some basic coding skills.

But with limited training, non-tech employees can build custom software applications to better meet customer demands in a matter of weeks or even days. And with less coding comes fewer issues, enabling non-IT teams to create secure applications that streamline business operations.

Shahzil Amin, WellBefore

Double-Check Effects on Page Speed

The low-code page builder I use for my website has enabled me to build out my site a lot over the last year, but my page speed and performance are not as high as they could be. An important thing to keep in mind is if a low-code solution will get you the results you need over time.

Wesley Jacobs, Apollo Medical Travel

The post 7 Things to Know About Low-Code Development Platforms appeared first on CIO Insight.

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What is Enterprise Security Management?

Posted in: cyber-threats, enterprise security management, ESM, IT security, Security, software failure - Aug 03, 2021

With enterprises moving towards new technologies to minimize costs and optimize resources, they face increased security risks as cybercriminals adopt new techniques to target BYOD devices, corporate networks, backend servers, and more. As a result, it has become crucial for stakeholders to understand how to balance the security management landscape with enterprise operations.

Organizations need to place more focus on enterprise security management (ESM) to create a security management framework so that they can establish and sustain security for their critical infrastructure. Enterprise security management is a holistic approach to integrating guidelines, policies, and proactive measures for various threats.

A Closer Look at ESM in the Enterprise

Understanding Enterprise Security Management

ESM pertains to all risks that may affect the core business of an organization. It includes failed software processes, inadvertent or deliberate mistakes committed by staff members, internal security threats, and external security threats. The concept also takes into account the following factors related to the security architecture framework.

Enterprise-Wide Compliance

The number of regulatory requirements can affect the end product/service delivery. The ESM framework aims to resolve conflicting business objectives, as well as fulfill regulatory and internal compliance requirements.

Business-Focused Outcome

In a standard ESM framework, security risks and company objectives drive the selection of security implementations. As it is a top-down architecture, it ensures the identification and control of all policies.

Clarity at Data-Infrastructure Level

The key challenge for the enterprise is to gain clarity and resolve conflicts pertaining to data privacy requirements, vulnerability vectors, and company objectives. The ESM approach to clarity enables the enterprise to gain transparency around the aforementioned, both at the infrastructure and data security level.

Transformation of Security at All Levels

ESM adopts the approach called “architecting a security framework at all levels” of an organization. It defines security capabilities from the governance level all the way through architecture, and involves planning to build, monitor, and deliver security within all organizational units, processes, and business functions.

Deploying an ESM Framework

All stakeholders will look to the CISO, CSO, or CIO to deploy and manage ESM frameworks, as well as the steps the organization is taking to reduce risk to the enterprise. How does a CIO integrate the ESM framework and cultivate a security culture that finds long-term success throughout the organization?

The answer lies in adopting a strategic approach towards enterprise security management. The following steps should be taken:

Patch Management

Software vulnerabilities are one of the leading issues in the enterprise environment. Patches are additional code to replace flaws in software. Patch management is part of the software development life cycle (SDLC) and can occur in any primary process of SDLC.

The importance of implementing patch management as a part of ESM is gaining value, especially due to a plethora of exfiltration and data breaches around the globe. Scanning and updating patches to prevent and mitigate undiscovered vulnerabilities is important and requires security management at all phases: QA, development, staging, and maintaining strict policies to avoid any unexpected events.

Threat Modeling

Who might attack the enterprise? Is it only cybercriminals, or nation-states as well? What about company insiders? Start thinking about the list of possible adversaries and get detailed, without ruling out outlandish ideas your team may come up with. Threat modeling requires the following steps:

Identification of security objectives
Company-wide survey
Decomposition
Identification of threats
Identification of vulnerabilities

Typically, a threat model takes longer to construct, but a sample structured list can be followed. Usually, the model is based on the following assumptions:

Data validation may enable SQL injection.
Authorization may fail, so authorization checks are required.
SSL should be used as the risk of eavesdropping is high.
Anti-caching directives should be implemented in HTTP headers, as the browser cache may contain man-in-the-middle vulnerabilities.

Architecture Principles

ESM never assumes that developing a threat model can provide sufficient risk mitigation for specific threats. It aims to deploy multiple controls in order to prevent and minimize damage while an enterprise responds. Architecture principles in ESM include the following:

Security Resiliency

Ensure security defenses throughout the organization by strengthening the resiliency of software, applications, networks, servers, and systems to recover from unforeseen circumstances.

Segregation

Security initiatives should be categorized into functional blocks, and organizational units will have distinct roles within each block to facilitate management and secure the critical infrastructure.

Regulatory Compliance and Efficiency

Industry best practices should be followed to achieve regulatory compliance. Efficient configuration throughout the infrastructure lifecycle and increased visibility will allow for faster troubleshooting, incident response, and auditing.

More on security auditing: Creating a Network Audit Checklist

Systemwide Confidentiality and Collaboration

Security controls need to include accepted levels of confidentiality, and effective infrastructure security will require correlation, collaboration, and sharing of information from all systemwide sources.

Risk Management

The compromise of R&D intelligence, customer data, and company secrets leads to the loss of millions of dollars in terms of trust, confidence, and monetary value. As such, enterprises must employ a risk management approach against targeted attacks.

Because conventional security implementations are no longer sufficient against techniques such as hacking, DDoS, botnet, state-sponsored espionage, and others, the latest ESM model includes the adoption of behavior detection and network virtualization to avoid becoming victims. It would be based on a custom defense strategy that utilizes a specific intelligence adapted to each enterprise and its potential attacker.

Additionally, risk management enforces stronger adoption of intelligence-based security solutions that are backed by reliable threat information sources. This will help enterprises to thwart attempts to vulnerabilities before patches are updated.

Combating DDoS and other attacks: Top Zero Trust Networking Solutions for 2021

MDM and Mobile Safety

With the inception of BYOD, many issues pertaining to data protection and control arise when an enterprise defines the lines between personal and corporate data. Other threats, such as data breaches through staff-owned devices and physical theft, are also an issue.

As a result, enterprise security management must address mobile device management (MDM) to protect enterprise data, devices, and apps. Administrators in the IT department should be able to centrally manage all device users from a centralized console, enabling visibility and increased mobile use safety.

SDN and IoT

In ESM, the security control layer needs to be centralized for different parts of the critical infrastructure. That is where software defined storage (SDS) and software defined networking (SDN) comes into play.

These two software strategies have been separated in the enterprise environment over the years, but need to come together in the future to deal with cyberthreats. Increased unity can reduce the damage across enterprise operational networks and industrial complexes.

Also, whatever air gaps and network segmentation methods an enterprise may have employed, there will be instances where the Internet of Things (IoT) intersects the enterprise network, and these touchpoints will be vulnerable to cyberattacks.

In fact, IoT can exacerbate the problem to a point where it gets messy to control internal and external networks and devices, especially when users are using all kinds of devices to access enterprise data stored in the cloud, BYOD applications, networks, and other places.

This means a hacker can get into a web-enabled device, and because of its connectivity with a corporate network, they can create a bridge to transfer malicious traffic back and forth.

These threats present an opportunity for enterprises to step in and implement security as a service in ESM for safeguarding those checkpoints and interactions, so the organization can continue to focus on gleaning security and corporate data.

Upgrading Your Security

The ESM market continues to change and grow, with a recent Markets and Markets study predicting a security and vulnerability management global market size of $15.5 billion by 2025. Companies will need to start investing to upgrade their security beyond checkbox implementations to achieve compliance-level protection. Enterprises keep IT security lean, in an attempt to cut operational costs.

ESM is a time-intensive exercise, and to keep every aspect of their company secure, organizations can’t afford to take any shortcuts. CIOs can use this information to make sure their organizations are adapting to the latest threats.

The post What is Enterprise Security Management? appeared first on CIO Insight.

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Is Biometric Technology Worth the Cost?

Posted in: biometric authentication, biometric data, biometric ID, biometric logins, biometric user authentication, biometrics, biometrics security, Infrastructure, IT Management, Security - Jul 30, 2021

The biometric technology market is estimated to grow to $68.6 billion by 2025, according to MarketsandMarkets. This growth can be attributed to the technology’s convenience, security, and scalability. But in spite of these benefits, this security technology can be costly.

What Is Biometric Technology?

Businesses often implement biometric technology for single-factor or multi-factor authentication purposes to ensure safe and secure access to networks and applications, among other things. Common biometric security implementations include fingerprinting, hand scanning, iris scanning, retina scanning, facial recognition, or voice recognition hardware and software.

How Much Does Biometric Security Cost?

A basic USB fingerprint scanner can cost as little as $20 per device. However, enterprises are more likely to need more sophisticated entry point security. “Prices for biometric access control systems range from a total of $2,500 to $10,000 per door when you factor in the biometric scanner, electronic locking system, software integration, and installation, according to VIZpin.

Higher adoption rates of biometric technology in general mean prices are likely to fall. In fact, they already have fallen. Ramped-up production due to increasing adoption during the COVID-19 pandemic reduced the price of many biometric access control systems.

Higher adoption rates mean prices are likely to fall.

Iris scanning, retina scanning, and facial recognition are the most expensive types to implement, followed by finger vein and voice recognition. Fingerprint recognition is the least expensive option in this space.

Each permutation of this technology comes with its own level of security, however. For instance, though iris scanning and voice recognition are among the most expensive types of biometric technology, they only offer low to medium security. Retina scanning is the most secure.

Conversely, fingerprint and voice recognition, though more cost efficient, offer relatively low security. Finger vein scanning devices are midrange in price, but offer high security. As such, finger vein scanning appears to be the current best bet, considering the cost-to-security ratio.

Pros & Cons of Biometric Technology

Weigh the following factors as you consider implementing biometric technology into your business:

Pros	Cons
Convenient and fast to use	Hygiene concerns for touched surfaces
More secure than ID cards or passwords	Potential for data breaches
Technology is constantly evolving	Invasiveness and privacy concerns
Falling prices	Currently costly to implement

Biometric security is convenient, as humans often forget passwords and lose access cards. By removing the need for passwords, badges, or keys, biometrics is a more secure option for ensuring sensitive areas and information are only available to authorized users.

Because of the popularity of this technology, biometric security is consistently improving. Also, that popularity is steadily decreasing prices.

Despite these advantages, there are drawbacks to biometrics. Though prices have been sinking, biometric security is still not a cheap investment. Prices vary wildly by device, as each has a different type of sensor.

Investment in a more secure method of measurement is generally worth it.

Additionally, users’ unique biological features are sensitive information. Privacy and secure storage is therefore of utmost importance. Though the technology is relatively secure, there is always the potential for data breaches. Investment in a more secure method of measurement is generally worth it.

Lastly, some of these devices require frequent contact, which raises questions of hygiene. “Long-established solutions like touch-based fingerprint recognition represent a risk in the context of infection spreading,” read a recent Biometrics Institute report addressing industry concerns around COVID-19. Therefore, contactless biometric solutions should be considered.

Depending on your business’ size and operation, some biometrics devices may be more feasible than others. Take the above factors into consideration when deciding whether you should implement biometric security.

Biometric Technology Trends

Here are some trends to watch out for as biometrics continues to evolve:

Ethical Concerns

As devices capture and store our physiological and behavioral data, questions of privacy and ethics will become increasingly important. Recent studies have exposed training biases in the AI used for voice and facial recognition software. Currently, the bio identifiers of many BIPOC, transgender, and nonbinary individuals are not reliably recognized by this software.

Read more: AI Software Trends for 2021

Behavioral Biometrics

Where traditional biometrics measures physical attributes, behavioral biometrics “identifies people according to how they interact with online applications and devices,” according to AI Business. Advances in machine learning, AI, and deep learning are allowing businesses to continually authenticate users using existing device sensors — such as accelerometers, gyroscopes, and touchscreens.

Going forward, organizations and government agencies that work with sensitive information may find it useful to employ physical biometric security at access points, as well as behavioral biometrics on company devices.

Biometrics-as-a-Service

Cloud solutions in biometrics, or Biometrics-as-a-Service (BaaS), will make this technology even more affordable and convenient for businesses to adopt. It will also make this technology more scalable.

According to a MarketsandMarkets report, BaaS “delivers biometric onboarding and authentication capabilities on the cloud platform and eliminates the cost associated with the database, network, and storage components. The only hardware component required is the biometric capture device to capture the individual biometric input, which makes it easier for these solutions to be deployed.”

The third-party software market that supports biometric technology is expected to grow exponentially, as well. It will become increasingly imperative to make this technology compatible across different devices and operating systems.

Biometric Technology Is Here to Stay

Not only is biometric technology is here to stay, but it’s worth investing in. As more entities opt for biometric security, and as technology continues to improve, this is the way forward for both enterprise and consumer products.

There are many implementation options available, and they may be layered to offer multi-factor authentication. Finger vein scanning seems to offer the best cost-to-security ratio currently, but this industry is growing.

The post Is Biometric Technology Worth the Cost? appeared first on CIO Insight.

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AI Software Trends for 2021

Posted in: adversarial machine learning, AI, AIOps, artificial intelligence, Big Data, BIPOC, Case Studies, data annotation, data fabric, data governance, data lakes, data quality, data warehouse, deepfake, ethics, facial recognition, Innovation, IT Management, IT Strategy, LGBT, machine learning, ML, MLOps, natural language processing, News & Trends, Security, voice recognition - Jul 29, 2021

Artificial intelligence (AI) and machine learning (ML) have quickly progressed from niche technology trends to frequent integration with business operations, new products and services, and customer service innovations across industries. According to Grand View Research, the artificial intelligence software market reached $62.3 billion in 2020 and is expected to grow exponentially, hitting $997.8 billion by 2028.

As the AI software market expands, there are several software trends we expect to see in the next several years: watch for increased automation, more intelligent security practices, and a better understanding of AI ethics.

Trends to Watch in Artificial Intelligence Software

The Evolution of AIOps and MLOps

Artificial intelligence and machine learning for IT operations, known as AIOps and MLOps respectively, are likely the two fastest-growing operational practices among major enterprises. They support a growing drive toward automation and consolidation of back-office operations, using machine learning and big data analytics to automate network monitoring, troubleshooting, and other network management tasks.

By using AI and ML to consolidate network management tools and limit the need for human action on basic operations, network administrators are free to spend more time on strategic network efforts. AIOps is growing quickly as people see the saved time and costs that it brings.

More on this topic: The Future of Network Management with AIOps

AI/ML to Automate Basic Cybersecurity Tasks

Certain tasks can and should be automated with AI and ML to decrease user error, and free up time for your team’s cybersecurity experts to focus on more complex issues. These are some of the top cybersecurity areas that can be automated:

Day-to-day security management
Threat spotting/network monitoring
Security log reading
Alerts for escalated threats

The key to successfully automating cybersecurity with AI and ML is developing and constantly improving upon the training data you feed into these systems. Without detailed protocols and training, your cybersecurity AI will miss key management, auditing, and alerting tasks that could jeopardize your network’s safety or make your ML scripts more susceptible to breach.

Growing Number of Data Quality Solutions

High-quality training data is the only way companies can really take advantage of AI solutions, which is why many are investing time and resources into cleaning up their data. This focus is not only on the legibility of data, but also on the overall compliance and scalability of that data:

Data governance tools are helping enterprises to ensure their training data adheres to all appropriate data protection laws and regulations.
Data annotation tools make qualitative, quantitative, structured, and unstructured data legible for ML technologies.
Smart data fabrics, data lakes, and data warehouses continue to grow as more enterprises recognize the need for big data storage space that also offers high levels of searchability.

In an interview with Datamation, Amy O’Connor, chief data and information officer at Precisely, explained why data quality is so important to the success of enterprise software.

“Some of the hottest tools these days are the ones typically considered to be the least sexy – quality profiling tools and data governance tools,” O’Connor said. “Tools that automate insights into the quality of data and enable that quality to be significantly improved through automation can have an exponential impact on the quality of analytical insights.”

Examining AI Ethics

As AI software grows in its capabilities and widespread use, developers, enterprises, and users alike have developed concerns about the ethics behind these tools. These are the areas of concern that have already arisen in AI ethics and some that will likely pose a problem for AI software vendors and users in the future:

Voice Recognition Software

According to recent studies, the voices of many BIPOC and non-native English speakers are not always picked up by the voice recognition of smart speakers and other natural language processing (NLP) software. The studies explain that voice recognition technology from Amazon, Apple, Google, IBM, and Microsoft misidentified 35% of words from Black individuals, with a significantly smaller margin of error for white users.

The study notes that the majority of developers behind this technology are white, and thus did not account for vocal or dialectical differences in the development of voice recognition technology.

Facial Recognition Software

Facial recognition technology poses consequential problems for BIPOC, transgender, and nonbinary communities. Simple — though still harmful — racist profiling and misgendering can occur for users, causing problems like medical misdiagnosis. Errors with this technology have even limited medical studies among certain communities.

But there are even greater safety concerns with this type of technology when government and law enforcement groups use computer vision. If an individual’s facial profile “matches” a certain minority group’s template profile in the system, many police and surveillance technologies are trained to watch their actions more closely in public. Innocents are even targeted for arrest in group gatherings.

The growing use of computer vision and its inherent biases pose concerns for the safety of minority and disadvantaged groups in settings like airport security and public protests — both spaces where minority groups are already discriminated against.

AI in the Wrong Hands

AI powers and simplifies many business processes, and even aids in humanitarian efforts like medical diagnosis and treatment. But what happens when powerful, humanoid technology gets into the hands of terrorists, warring factions, and other malevolent actors?

We’ve already seen the earliest developments of adversarial machine learning, or the practice of hacking into ML technology and changing its internal script or actions. Such a breach can have negligible impact in some cases, but it can also have dire consequences: driving a self-driving car off the road or activating an AI-powered military drone, for example.

There’s also the development of generative adversarial networks and synthetic content generation, colloquially known as “deepfakes.” This artificial manipulation and production of media raises many concerns about copyright, creative ownership, and the spread of disinformation. Adversarial AI capabilities continue to grow, so the question becomes: are we creating technology that does more damage than good?

How AI Impacts Human Resources: HRIS Trends for 2021: The Future of HR Management

The post AI Software Trends for 2021 appeared first on CIO Insight.

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Top Big Data Tools & Software for 2021

Posted in: Big Data, big data analytics, big data analytics solutions, Cloudera, Enterprise Apps, IBM DB2, IT Strategy, Oracle Big Data SQL, Qubole, SAP, SAS Enterprise Miner, Scality, Tintri - Jul 28, 2021

Big data tools typically seek to combine the functions of storage and analytics/BI. This is because the model of gathering data in one repository and sending it to an external analytics engine tends to break down as the volume of data soars.

With so much unstructured data available from so many channels, it makes sense to combine storage and analytics into one system. As such, analytics tools now have added storage capabilities; conversely, traditional storage vendors have expanded into the realm of analytics.

The market therefore includes a hodgepodge of products. Some are focused on analytics, others on storage with analytics capabilities. More than a few tools perform one of the two functions, but integrate closely with another vendor to provide full big data analytics functionality.

Big Data Tools Key Features

Due to the way the market has evolved, the feature sets vary widely. The following core features are found in most systems:

Large-scale storage: Most big data analytics tools house a LOT of storage. Yes, some are larger than others. Some take advantage of larger storage repositories provided by partners. But regardless of the architecture, these tools must still be able to handle a lot of data.
Analytics engine: Gathered data needs to be analyzed. Some analytics engines are better than others, as they have been at it longer. But even relative newcomers to this space still need to have decent analytics capabilities built in.
Data cleansing: Analytics results depend on the quality of the data. Veteran analytics companies are skilled at data cleansing. Those newer to the field are still finding their feet in this arena.
In memory: To speed the process of coming up with a result, some data sets or subsets are able to sit in memory. By analyzing the data there, insights can be derived in a fraction of the time it takes to do analysis of disk-based data.

Top Big Data Tools and Software

CIO Insight evaluated the various vendors in big data analytics. Here are our top picks, in no particular order:

SAS Enterprise Miner

Value Proposition

SAS Enterprise Miner is one of many analytics tools within the SAS arsenal. It aims to dramatically shorten model development time for data miners and statisticians. An interactive, self-documenting process flow diagram environment efficiently maps the entire data mining process to produce the best results. The company claims this big data tool offers more predictive modeling techniques than any other commercial data mining package.

Key Differentiators

Batch processing
Data preparation, summarization, and exploration
Preparation tools address missing values, filter outliers, and develop segmentation rules
Predictive and descriptive modeling
Suite of statistical, data mining, and machine-learning algorithms
Open source integration with R
High-performance data mining nodes
SAS Rapid Predictive Modeler steps nontechnical users through data mining tasks
Model comparisons, reporting, and management
Automated scoring in SAS, C, Java, and PMML,
Scoring code is deployable in SAS, on the web, or directly in relational databases or Hadoop.

IBM Db2 Big SQL

Value Proposition

IBM Db2 Big SQL is an enterprise-grade, hybrid ANSI-compliant SQL-on-Hadoop engine, delivering massively parallel processing and advanced data query. This data virtualization tool is for accessing, querying, and summarizing data across the enterprise. It offers a single database connection or query for disparate sources, such as Hadoop HDFS and WebHDFS, RDMS, NoSQL databases, and object stores.

Key Differentiators

Can be integrated with Cloudera Data Platform, or accessed on IBM Cloud Pak for Data
Enterprise-grade SQL-on-Hadoop performance using elastic boost technology
Low latency with support for ad-hoc and complex queries
High performance, security, and federation capabilities
Hybrid Hadoop engine exploits Hive, HBase, and Apache Spark concurrently
Role-based access control, row-based dynamic filtering, column-based dynamic masking, and Apache Ranger integration
Standards-compliant Open Database Connectivity and Java Database Connectivity
Allows access to the database with specific products or tooling that allow only Open Database Connectivity or Java Database Connectivity

Cloudera Enterprise Data Hub

Value Proposition

Cloudera Enterprise Data Hub (EDH) delivers an integrated suite of analytic engines ranging from stream and batch data processing to data warehousing, operational database, and machine learning (ML). It works in conjunction with Cloudera SDX, which applies security and governance. This enables users to share and discover data for use across workloads.

Key Differentiators

Accelerates ML from research to production
Built-in data warehouse delivers a cloud-native, self-service analytic experience
Data warehouse is integrated with streaming, data engineering, and ML analytics
Governance for all data and metadata on private, public, or hybrid clouds
Operational database-as-a-service brings flexibility to Apache HBase
Database management capabilities like auto-scale, auto-heal, and auto-tune
Integrations with Cloudera Data Platform and services
CDP Data Engineering built on Apache Spark for automation with Apache Airflow, advanced pipeline monitoring, visual troubleshooting, and management tools

Oracle Big Data Service

Value Proposition

Oracle Big Data Service is a Hadoop-based, managed service that includes a data lake, a data warehouse, and more. The Oracle Autonomous Data Warehouse is part of it. This cloud data warehouse eliminates all the complexities of operating a data warehouse, securing data, and developing data-driven applications.

Key Differentiators

Automates provisioning, configuring, securing, tuning, scaling, and backing up of data warehouse
Tools for self-service data loading, data transformations, business models, and automatic insights
Converged database capabilities enable simpler queries across multiple data types and ML analysis
Oracle big data services via Lake House
Object storage and Hadoop-based data lakes for persistence and Spark for processing
Analysis through Oracle Cloud SQL or the analytical tool of choice
Cloud Infrastructure Data Flow is a managed Apache Spark service with no infrastructure to deploy or manage
Cloud Infrastructure Object Storage enables storage of data in its native format
Cloud Infrastructure Data Catalog helps search, explore, and govern data
Cloud Infrastructure Data Integration extracts, transforms, and loads data

Qubole

Value Proposition

Qubole offers a secure and open data lake platform to accelerate machine learning, streaming, and ad hoc analytics. Its customer base includes Expedia, Disney, Lyft, and Adobe. Instead of proprietary formats, proprietary SQL extensions, proprietary metadata repository, and lack of programmatic access to data, the Open Data Lake has no vendor lock-in while supporting a diverse range of analytics.

Key Differentiators

Author, save, template, and share reports and queries via Workbench
Build data pipelines combining multiple streaming or batch data sources via Assisted Pipeline Builder
Offline editing, multi-language interpreter, and version control capabilities
Qubole Notebook monitors application status and job progress
Secure access with encryption and RBAC controls
Build and manage metadata, explore data dependencies, and provide indices and statistics

Scality RING

Value Proposition

Scality RING protects data at scale. The distributed architecture is redundant without bottlenecks to scale out to dozens of petabytes of capacity in a single system. It integrates file and object storage for workloads focused on high-capacity unstructured data. Data is protected even against data center outages through geo-distribution.

Key Differentiators

Scalable data lake that can be accessed by applications such as Hadoop and Spark
Runs on-premises on commodity hardware and extends into the public cloud
Certified by over 100 ISV solutions
Enterprise-grade data durability, self-healing, security, encryption, and multi-tenancy
Integrated hybrid-cloud data management to AWS, Azure and Google via XDM
Built-in cloud archiving, bursting and disaster recovery solutions.
POSIX compatible file system, with standard NFS v4/v3 and SMB 3.0 file interfaces
Policy-based data replication and erasure-coding for up to eleven 9s data durability
Integrated hybrid-cloud data management
Encompasses multi-cloud namespaces, native Azure object storage support, and bidirectional compatibility with S3

SAP BTP

Value Proposition

The SAP Business Technology Platform (BTP) is an integrated offering comprised of four technology portfolios: database and data management, application development and integration, analytics, and intelligent technologies. SAP Database and Data Management enable control of the data landscape with an end-to-end view of all data through a single gateway. SAP databases securely provide transactional and analytical processing across on-premises, hybrid, and multi-cloud environments. SAP is used by a huge number of organizations around the world to manage data, ranging from global businesses to data-centric SMEs.

Key Differentiators

In-memory analytics and processing in near-real time
Huge collection of database tools such as SAP HANA, SAP HANA Cloud, SAP IQ, more
Data management tools include SAP Information Steward, SAP PowerDesigner, more
Cloud database management
Governance of data including compliance and privacy
Consistently highly ranked in Gartner Magic Quadrants (MQ) and Forrester Wave analyses

Tintri

Value Proposition

Tintri offers an intelligent storage platform featuring AI-driven autonomous operations, app-level visibility, and analytics to drive down storage management costs by up to 95%. Set up groups, then apply data protection and service level policies that apply with no manual intervention required.

Key Differentiators

Cloud-based SaaS solution
Tintri Global Center (TGC) automatically controls apps
Set policies for service levels, cloning, snapshots, and replication
Federates up to 64 VMstore all-flash systems
Crunches one million stats about apps every ten minutes
Troubleshoot latency in seconds across host, network, and storage
ML algorithms model every storage and compute need for up to 18 months into the future
Historical metadata is used to predict capacity, performance, working set, and compute needs

The post Top Big Data Tools & Software for 2021 appeared first on CIO Insight.

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