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Clinical trials are a growing cross-section of modern medicine and pharmaceuticals, expecting significant growth in the coming years. The valuation of is $44.3B in 2020, with projected growth of 5.7% annually until 2028, show the models of Grand View Research. global clinical trials Clinical trials have several avenues of growth, including personalized medicine. The market for is not only going to expand beyond the average growth for all trials, but pose additional challenges of data collection and personal data preservation. The growth in this market is expected to be 11.7% on an annual basis in the near future, outpacing the pipeline for other clinical trials. precision medicine An additional pressure will be placed on the clinical trial sector by the uncertainties surrounding Covid-19 and the longer-term effect of the pandemic. In 2020, faced direct headwinds and some were canceled. In the future, safety precautions and stricter tracing may be the hallmarks of post-Covid trial organization. patient trials Within this climate, clinical trial startups are attempting to fine-tune the use cases for blockchain record-keeping. We looked at several projects and their technique to tackle the complexity of personal and medical data with the technical strengths and weaknesses of blockchain technology. By preliminary estimates, blockchain tech may add up to $3B in value to the sector until 2025, shows intelligence by . The trends tracker estimates up to 70% of leaders in life sciences research will acquire blockchain functionalities in the coming years. PreScouter 1. Innoplexus specializes in Life Sciences research, offering tools that combine artificial intelligence and blockchain. Based on the expertise of a 250-strong team, Innoplexus offers solutions for drug discovery and clinical trials. Innoplexus Innoplexus has a multi-stage process to tailor research designs for its clients. At the core is its AI-powered data crawling mechanism especially built for biological and medical sciences. Data normalization and ontology building to supply the AI tools with data also add to the research design. Blockchains are tailor-made for clients, relying on distributed, but private communication. Due to the sensitive nature of the data, private blockchains ensure constant communication between a selected group of partners. Innoplexus specializes in data-driven drug creation, especially for early-stage biotechnology companies. On the side of communication and blockchain, Innoplexus offers full GDPR compliance, as well as patient privacy through data anonymization. 2. ClinTex ClinTex aims to provide data and blockchain services where the pharmaceutical industry is its primary customer. The driving idea behind ClinTex is to improve the efficiency of clinical trials through technology. By its own estimate, suggests clinical trials may be running up inefficiencies to the tune of $600,000-$8M per day, while also delaying the launch of valuable drugs to market. ClinTex ClinTex already launched the Clinical Trials Intelligence token (CTi) toward the end of 2020. To boost adoption, ClinTex incentivizes current token holders through a six-month staking program. During that time, locked tokens will earn up to 40% interest if locked for the full period. The additional 18.5M tokens, worth close to $1M, will be distributed to early investors that acquired CTi during the early stages of product development. CTi will be the only asset available to use the Clinical Trials Intelligence platform in the future to gain access to clinical trial data. CTi is available both as an Ethereum-based token and as an asset on Binance Smart Chain, a lower-fee network for faster transfers. With its drive for adoption, ClinTex combines a publicly tradable token, available on KuCoin, with a tailored solution to build faster, cheaper clinical trials. Interest in the project has boosted the price of CTi since March 2021, riding on the bull market trend for tokens. 3. Embleema is another service offering to build clinical trials from scratch, including the design and participant recruitment. This startup has built its proprietary Virtual Study Suite to generate research design in an intuitive, traceable fashion. Embleema Blockchain communication then ensures confidential, secure and easily available information. Embleema already tracks data on several socially significant conditions among the US population, as well as gathering new intelligence on Covid-19 incidence and effects. Embleema is notable for running HIVE, the only US FDA-approved information system for biological big data and genomic information. Through HIVE, FDA has performed numerous drug or genomics studies, after full regulatory approval of the platform. The Embleema project is also the founder of the Health Blockchain Consortium, aggregating startups that match biotechnology data with blockchains. Embleema is one of the projects using private blockchain capabilities, without issuing a public digital asset. 4. Doc.AI aims to be a hub for data to serve the need of insurers, healthcare providers, and pharmaceutical companies. The project removes barriers to entry, by allowing users to generate immediate mobile-based, fully functional studies, reaching millions of potential respondents. Doc.AI Doc.AI was created in 2016 with the aim of building both researcher and consumer-directed apps. In 2017, the Silicon Valley startup started one of its first tests of blockchain technology. The chief aim of Doc.AI was to protect and encrypt user data, thus preserving the anonymity of survey respondents, while granting access to researchers. With Doc.AI, respondents can choose to participate in open studies for socially important health conditions. There is also the option to train medical AI with data, with the goal of anticipating seasonal allergies. 5. Triall is also on a mission to streamline medical trials and deliver new pharmaceuticals faster. The chief challenge of contemporary researchers is the need to make trials fully digital. Triall has created a network of microservices based on blockchain communication, to build a system of connections between the various processes and data sources in a clinical trial. Triall Triall is already operating one complete clinical trial product, Verial eTMF, with six research partners. Through its consulting services, the startup has also worked with more than 100 international clinical trials. Triall also has a lightweight product, Triall CTMS, to create and manage clinical trials. Additionally, the team has launched Atena PRM, a vendor selection software utilizing blockchain-based voting to discover the best trial vendors. Triall is still in a token pre-sale stage, with few details known about its upcoming digital asset. For now, the startup is still gathering a pre-listing database of potential stakeholders and investors. 6. BioPharma Ledger by Alten Calsoft Labs BioPharma Ledger is a specially created blockchain platform, aiming to streamline the processes of research management. The platform operates all major elements of digital life sciences, including patient data privacy, FDA submissions, clinical trial data management and other processes between patients and providers. BioPharma Ledger offers specialized, private blockchain solutions, which emulate the Ethereum smart contract capabilities. The biopharmaceutical startup was a branch of Alten Calsoft Labs, a digital transformation solutions provider. The blockchain trial data solution branched out in 2019, among rising interest in blockchain technology. BioPharma Ledger is aimed at the international clinical trials market. One of its first partners is Cinlogix, the originator of many clinical trials in Europe, Asia, South America, and the USA. BioPharma Ledger is also part of the Enterprise Ethereum Alliance (EEA), an open-source collaboration on blockchain technology. For now, BioPharma Ledger relies on private blockchain solutions, with no goal of issuing its own tokenized assets. Blockchain additions to clinical trials thus branch out depending on the project’s vision. The latest addition follows the general digital transformation of the sector, and can add new incentives and money-saving tools, alongside a confidential, error-proof system to access data.

6 Medical Blockchain Solutions for Clinical Trials

Spatial computing is beginning the slow crawl to maturity. As the technology develops into something simultaneously more complex and also more stable, new areas of possibility open and grow. , but it’s only now that real solutions are starting to be feasible. Since the beginning, avid enthusiasts have seen hope of a reformation of the real estate industry with the help of spatial computing Augmented reality projection rooms. Virtual reality walkthroughs. Mixed reality remodelling. These are just some of the potential ways that the act of buying and selling property will begin a seismic shift. — plus, our breakdown of how successful we think they will be. We’ve collected five different projects that are using spatial computing to revamp real estate #1. 360 Walkthrough The idea of a , but until recently the technology has limited these “previews” to a flat experience. You could view a home on your computer, running around a space using the same drag and drop technology that Google Maps used to let us explore the world. Thanks to head mounted displays, those 360 walkthroughs are now even more immersive, and because 360 cameras have gotten so cheap and easy to use, every realtor can and should be adding a 360 walkthrough to their listings. 360 walkthrough isn’t new Because the cost of entry is so low, 360 walkthroughs are still a smart place to start, but this technology is quickly being overshadowed. There’s no real sense of immersion, since your point of view is limited to wherever the camera was initially placed; viewers don’t have the freedom to walk around, putting themselves in corners or experiencing the room from any angles the camera didn’t capture. We doubt this technology will survive more than five years, though for now the cost of hardware for alternative options will keep it afloat. #2. Lightfield Volume Capture Speaking of alternative options — we’re a little obsessed with light field volume capture at Hammer & Tusk, but it’s hard not to be. These cameras capture 360 video with 6 degrees of freedom, which means they can reproduce a location in photorealistic detail, and then let a person wander through that space. While creating video with this technology is still in the “so many hurdles to overcome” camp, the great news is that still pictures are ready for their mainstream debut. There are a ton of companies working on this hardware, but none that we know of specializing specifically in adapting it for real estate. It’s a wide open playing field. #3. Building in a Virtual World Getting to consumers is a huge part of real estate, but what about creating buildings in the first place? If you’ve ever seen someone painstakingly hand-paint 400 tiny fake trees to put on the outside of an architectural model, you know the discipline is rife for disruption. Enter software like Vividly, which lets you create life-sized 3D models of real estate. Walk through your creations, share them with colleagues or clients, and even convert existing 3D models from traditional architectural software. The world is your oyster! #4. Remodelling the Future Okay, so you’ve bought a house, but now you want to see what it would look like with granite countertops instead of marble; or maybe you want to knock down that wall into the kitchen and create a pass-through. If that’s the future you’re imagining, you’re not alone! So many people are entering this space that it’s hard to call out just one or two. will be taking on remodelling thanks to Apple’s ARKit, which will improve the quality and ease-of-use of previous offerings that IKEA has tested over the years. IKEA is a less proprietary company; no matter which construction company you’re using, or where you’re getting your fixtures, Cadsoft will create 3D renderings and provide them for viewing in a headset or on your computer. VR viewings avoids the mobile-first issue of realism, but adds on the headache of needing the right hardware, which is solved by the backup plan of computer mockups. Cadsoft The upside of this technology is that you don’t have to try to picture how a remodel will look; the downside being that you still need some imagination to turn these digital creations into their life-life alternatives. #5. The CAVE This technology is not technically being used for real estate today, but it has huge potential for future use-cases. CAVE is an open-source software system paired with projector hardware that primarily exists in university research settings. Think of it has a modern Holodeck — a large room whose walls are actually rear-projection screens or flat panel displays. The images on the walls are controlled by the motion of the person inside the CAVE, allowing a sense of real immersion as the room moves realistically along with the user. The applications for real estate are incredible. Imagine going to your realtor’s office, and instead of driving around the city to thirty showings, you could walk into the CAVE and see every home from there. You would walk around a pre-filmed environment, check out the layout, even peer into cupboards! As long as a camera captured it, you could view it. The strength of this technology is the ‘teleportation’ factor, and the ability to have more than one person experiencing the virtual environment at the same time. Downsides are that you’re still restricting to four walls and a small space, so things like moving up and down stairs will never feel entirely real. Plus, these setups require motion-capture technology and head-mounted displays, so comfort and setup would be a concern in a commercial environment. Academic institutions aren’t the only ones working on tech like this. We’re seeing , too. commercial applications Originally written by Wren Handman for www.hammerandtusk.com .

Selling the World, Virtually

CData Power BI Connectors provide self-service integration with Microsoft Power BI. The CData Power BI Connector for Google Sheets links your Power BI reports to real-time Google Sheets data. You can monitor Google Sheets data through dashboards and ensure that your analysis reflects Google Sheets data in real-time by scheduling refreshes or refreshing on demand. This article details how to use the Power BI Connector to create real-time visualizations of Google Sheets data in Microsoft Power BI Desktop. If you are interested in publishing reports on Google Sheets data to PowerBI.com, refer to our other article. Knowledge Base Collaborative Query Processing The CData Power BI Connectors offer unmatched performance for interacting with live Google Sheets data in Power BI due to optimized data processing built into the connector. When you issue complex SQL queries from Power BI to Google Sheets, the connector pushes supported SQL operations, like filters and aggregations, directly to Google Sheets and utilizes the embedded SQL Engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can visualize and analyze Google Sheets data using native Power BI data types. Connect to Google Sheets as a Power BI Data Source Installing the Power BI Connector creates a DSN (data source name) called CData Power BI Google Sheets. This the name of the DSN that Power BI uses to request a connection to the data source. Configure the DSN by filling in the required connection properties. You can use the Microsoft ODBC Data Source Administrator to create and configure the DSN: From the Start menu, enter "ODBC Data Sources" and select the CData PowerBI REST DSN. Ensure that you run the version of the ODBC Administrator that corresponds to the bitness of your Power BI Desktop installation (32-bit or 64-bit). You can also use run the ConfigureODBC.exe tool located in the installation folder for the connector. You can connect to a spreadsheet by providing authentication to Google and then setting the Spreadsheet connection property to the name or feed link of the spreadsheet. If you want to view a list of information about the spreadsheets in your Google Drive, execute a query to the Spreadsheets view after you authenticate. ClientLogin (username/password authentication) has been officially deprecated since April 20, 2012 and is now no longer available. Instead, use the OAuth 2.0 authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app. OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values. See the Getting Started chapter in the help documentation to connect to Google Sheets from different types of accounts: Google accounts, Google Apps accounts, and accounts using two-step verification. Query Google Sheets Tables Follow the steps below to build a query to pull Google Sheets data into the report: Open Power BI Desktop and click Get Data -> Other -> CData Google Sheets. Select CData PowerBI Google Sheets in the Data Source Name menu and select a data connectivity mode. Select Import if you want to import a copy of the data into your project. You can refresh this data on demand. Select DirectQuery if you want to work with the remote data. Select tables in the Navigator dialog. In the Query Editor, you can customize your dataset by filtering, sorting, and summarizing Google Sheets columns. Click Edit to open the query editor. Right-click a row to filter the rows. Right-click a column header to perform actions like the following: Change column data typesRemove a columnGroup by columns Power BI detects each column's data type from the Google Sheets metadata retrieved by the connector. Power BI records your modifications to the query in the Applied Steps section, adjusting the underlying data retrieval query that is executed to the remote Google Sheets data. When you click Close and Apply, Power BI executes the data retrieval query. Otherwise, click Load to pull the data into Power BI. Create Data Visualizations After pulling the data into Power BI, you can create data visualizations in the Report view by dragging fields from the Fields pane onto the canvas. Follow the steps below to create a pie chart: Select the pie chart icon in the Visualizations pane. Select a dimension in the Fields pane: for example, Shipcountry. Select a measure in the Fields pane: for example, OrderPrice. You can change sort options by clicking the ellipsis (...) button for the chart. Options to select the sort column and change the sort order are displayed. You can use both highlighting and filtering to focus on data. Filtering removes unfocused data from visualizations; highlighting dims unfocused data. You can highlight fields by clicking them: You can apply filters at the page level, at the report level, or to a single visualization by dragging fields onto the Filters pane. To filter on the field's value, select one of the values that are displayed in the Filters pane. Click Refresh to synchronize your report with any changes to the data. At this point, you will have a Power BI report built on top of live Google Sheets data. Learn more about the CData Power BI Connectors for Google Sheets and download a free trial from the . Let our Support Team know if you have any questions. Google Sheets Power BI Connector page

A Guide to Authoring Power BI Reports on Real-Time Google Sheets Data

Connect to Salesforce from AWS Glue jobs using the CData JDBC Driver hosted in Amazon S3. AWS Glue is an ETL service from Amazon that allows you to easily prepare and load your data for storage and analytics. Using the PySpark module along with AWS Glue, you can create jobs that work with data over JDBC connectivity, loading the data directly into AWS data stores. In this article, we walk through uploading the CData JDBC Driver for Salesforce into an Amazon S3 bucket and creating and running an AWS Glue job to extract Salesforce data and store it in S3 as a CSV file. Upload the CData JDBC Driver for Salesforce to an Amazon S3 Bucket In order to work with the CData JDBC Driver for Salesforce in AWS Glue, you will need to store it (and any relevant license files) in an Amazon S3 bucket. Open the Amazon S3 Console. Select an existing bucket (or create a new one). Click UploadSelect the JAR file (cdata.jdbc.salesforce.jar) found in the lib directory in the installation location for the driver. Configure the Amazon Glue Job Navigate to ETL -> Jobs from the AWS Glue Console. Click Add Job to create a new Glue job. Fill in the Job properties:Name: Fill in a name for the job, for example: SalesforceGlueJob.IAM Role: Select (or create) an IAM role that has the AWSGlueServiceRole and AmazonS3FullAccess permissions policies. The latter policy is necessary to access both the JDBC Driver and the output destination in Amazon S3.Type: Select "Spark".Glue Version: Select "Spark 2.4, Python 3 (Glue Version 1.0)".This job runs: Select "A new script to be authored by you". Populate the script properties: A name for the script file, for example: GlueSalesforceJDBC Fill in or browse to an S3 bucket. Fill in or browse to an S3 bucket. Expand . For Dependent jars path, fill in or browse to the S3 bucket where you uploaded the JAR file. Be sure to include the name of the JAR file itself in the path, i.e.: s3://mybucket/cdata.jdbc.salesforce.jarClick Next. Here you will have the option to add connection to other AWS endpoints. So, if your Destination is Redshift, MySQL, etc, you can create and use connections to those data sources. Click "Save job and edit script" to create the job. In the editor that opens, write a python script for the job. You can use the sample script (see below) as an example. Script file name: S3 path where the script is stored: Temporary directory: Security configuration, script libraries and job parameters (optional) Sample Glue Script To connect to Salesforce using the CData JDBC driver, you will need to create a JDBC URL, populating the necessary connection properties. Additionally, you will need to set the property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property. RTK There are several authentication methods available for connecting to Salesforce: Login, OAuth, and SSO. The Login method requires you to have the username, password, and security token of the user. If you do not have access to the username and password or do not wish to require them, you can use OAuth authentication. SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and TokenUrl connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the help documentation for more information. Built-in Connection String Designer For assistance in constructing the JDBC URL, use the connection string designer built into the Salesforce JDBC Driver. Either double-click the JAR file or execute the JAR file from the command-line. view source java -jar cdata.jdbc.salesforce.jar Fill in the connection properties and copy the connection string to the clipboard. To host the JDBC driver in Amazon S3, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), . contact our sales team Below is a sample script that uses the CData JDBC driver with the PySpark and AWSGlue modules to extract Salesforce data and write it to an S3 bucket in CSV format. Make any necessary changes to the script to suit your needs and save the job. view source sys awsglue.transforms * awsglue.utils getResolvedOptions pyspark.context SparkContext awsglue.context GlueContext awsglue.dynamicframe DynamicFrame awsglue.job Job
 
args = getResolvedOptions(sys.argv, [ ])
 
sparkContext = SparkContext()
glueContext = GlueContext(sparkContext)
sparkSession = glueContext.spark_session
 
##Use the CData JDBC driver to read Salesforce data the Account table into a DataFrame
##Note the populated JDBC URL and driver = sparkSession.read.format( ).option( , ).option( , ).option( , ).load()
 
glueJob = Job(glueContext)
glueJob.init(args[ ], args)
 
##Convert DataFrames to AWS Glue import from import from import from import from import from import from import 'JOB_NAME' from class name source_df "jdbc" "url" "jdbc:salesforce:RTK=5246...;User=username;Password=password;SecurityToken=Your_Security_Token;" "dbtable" "Account" "driver" "cdata.jdbc.salesforce.SalesforceDriver" 'JOB_NAME' 's DynamicFrames Object
dynamic_dframe = DynamicFrame.fromDF(source_df, glueContext, "dynamic_df")
 
##Write the DynamicFrame as a file in CSV format to a folder in an S3 bucket.
##It is possible to write to any Amazon data store (SQL Server, Redshift, etc) by using any previously defined connections.
retDatasink4 = glueContext.write_dynamic_frame.from_options(frame = dynamic_dframe, connection_type = "s3", connection_options = {"path": "s3://mybucket/outfiles"}, format = "csv", transformation_ctx = "datasink4")
 
glueJob.commit() Run the Glue Job With the script written, we are ready to run the Glue job. Click Run Job and wait for the extract/load to complete. You can view the status of the job from the Jobs page in the AWS Glue Console. Once the Job has succeeded, you will have a CSV file in your S3 bucket with data from the Salesforce Account table. Using the CData JDBC Driver for Salesforce in AWS Glue, you can easily create ETL jobs for Salesforce data, whether writing the data to an S3 bucket or loading it into any other AWS data store.

How to Connect to Salesforce Data in AWS Glue Jobs Using JDBC

Easily back up Smartsheet data to SQL Server using the SSIS components for Smartsheet. Using SQL Server as a backup for critical business data provides an essential safety net against loss. Backing up data to SQL Server enables business users to more easily connect that data with features like reporting, analytics, and more. This example demonstrates how to use the CData SSIS Tasks for Smartsheet inside of a SQL Server SSIS workflow to transfer Smartsheet data into a Microsoft SQL Server database. Add the Components To get started, add a new Smartsheet source and SQL Server ADO.NET destination to a new data flow task. Create a New Connection Manager Follow the steps below to save Smartsheet connection properties in a connection manager. In the Connection Manager window, right-click and then click New Connection. The Add SSIS Connection Manager dialog is displayed. In the Connection Manager type menu, select Smartsheet. The CData Smartsheet Connection Manager is displayed. Configure connection properties. Smartsheet uses the OAuth authentication standard. To authenticate using OAuth, you will need to register an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. However, for testing purposes you can instead use the Personal Access Token you get when you create an application; set this to the OAuthAccessToken connection property. Configure the Smartsheet Source Follow the steps below to specify the query to be used to extract Smartsheet data. Double-click the Smartsheet source to open the source component editor. In the Connection Manager menu, select the connection manager previously created. Specify the query to use for the data extraction. For example: SELECT TaskName, Progress FROM Sheet_Event_Plan_Budget view source Close the Smartsheet Source control and connect it to the ADO.NET Destination. Configure the SQL Server Destination Follow the steps below to specify the SQL server table to load the Smartsheet data into. Open the ADO.NET Destination and add a New Connection. Enter your server and database information here. In the Data access mode menu, select "table or view". In the Table Or View menu, select the table or view to populate. Configure any properties you wish to on the Mappings screen. Run the Project You can now run the project. After the SSIS Task has finished executing, your database will be populated with Smartsheet data. Also published on: https://www.cdata.com/kb/tech/smartsheet-ssis-task-import-2008.rst

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