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Customer Order Frequency

This scenario involves understanding customer order frequency, specifically determining the count of customers who made varying numbers of orders. While calculating the number of orders per customer is straightforward, discerning how many customers placed one, two, or multiple orders requires breaking down the count of customers based on order frequency. Utilizing LOD (Level of Detail) Expressions becomes essential in transforming the count of orders into a dimension that segregates customers by their order count. This process aids in unraveling insights about customer behavior in relation to their order frequency within a sales database where multiple items are present per order.

Cohort Analysis

In the pursuit of understanding the impact of customer tenure on sales contributions, cohort analysis is employed to assess whether longer-tenured customers hold more significant sales influence. The presented view categorizes customers based on the year of their initial purchase, facilitating an annual comparison of sales contributions among these cohorts. To determine the first purchase date for each customer, the minimum order date per customer is crucial. However, as the displayed data isn’t structured by customer, employing an LOD (Level of Detail) Expression becomes necessary to establish and retain the minimum order date per individual customer for accurate cohort analysis.

Daily profit KPI

In evaluating daily profit as a key performance indicator (KPI), the focus shifts from observing profit trends over time to quantifying success based on total profit per business day. Understanding the count of profitable days per month or year becomes essential, particularly in investigating potential seasonal impacts. Utilizing LOD (Level of Detail) Expressions, this view demonstrates the seamless creation of bins for aggregated data, like profit per day, despite the underlying data being recorded at a transactional level. This approach allows for efficient analysis and visualization of daily profitability trends within the context of the broader business calendar.

Percent of Total

Determining each country's revenue contribution to global sales is crucial for assessing market performance. When visualized by coloring contributions as percentages, it's apparent that the US holds the highest share of global sales revenue. However, focusing on markets like the EU, which might have a relatively smaller absolute contribution, becomes challenging without LOD Expressions. Without this capability, filtering by market could lead to recalculating the percent of total, displaying each country's contribution relative to its market. Using a straightforward LOD Expression enables filtering by market while preserving the measurement of each country's global contribution, facilitating a more nuanced analysis of market performance within the broader global context.

New customer acquisition

Analyzing the daily trend of total customer acquisition across different markets serves as a crucial metric in assessing the effectiveness of regional marketing and sales efforts in generating new business. By tracking this trend, we gain insights into the performance of these organizations. A steeper line signifies a stronger acquisition trend, while a flattening line suggests a need for increased lead flow.

To accurately measure this trend, it's imperative to ensure that repeat customers aren't erroneously counted as new customers. This necessitates using an LOD (Level of Detail) Expression, allowing data evaluation at the customer level despite its visual representation being segmented by market and day. This meticulous approach ensures a precise assessment of new customer acquisition, enabling strategic actions to be taken based on the observed trends.

Comparative Sales Analysis

When aiming to determine the difference from a selected category rather than the average, the process becomes more intricate. Initially, isolating the sales figures of the chosen category is necessary. Subsequently, employing an EXCLUDE Expression becomes crucial to reiterate that value across all other categories. This technique enables a straightforward calculation of the difference between each category's sales and the rest, allowing for a comparative sales analysis that emphasizes the disparity between the selected category and others.

Average of top deals by sales rep

Determining the largest deal closed by each sales representative and subsequently computing the average of these top deals by country is a multi-layered analysis. LOD (Level of Detail) Expressions play a pivotal role in dissecting data down to the sales rep level, even when the visualization displays information at the country level.
The presented view showcases the average top deal size by sales rep, offering insights where countries colored blue exhibit higher average top deal sizes, while those colored orange indicate comparatively lower averages. This information serves as a guide for further drill-down analysis from the country level to the sales rep level, facilitating a deeper understanding of performance variations across both geographical and individual sales rep perspectives.

Actual vs. Target

Within this visualization, we present the variance between actual and target profits per state for a chain of coffee houses. The top view distinctly showcases states surpassing or falling short of set targets. Yet, this aggregated view might overlook subtleties: some states exceed targets due to every product sold meeting or exceeding goals, while others rely on a single product surpassing its target to compensate for others missing theirs. Employing an LOD Expression enables the identification of the percentage of products sold within a state that surpass their set targets, offering a more nuanced assessment.

Value on the Last Day of a Period

Data reflecting specific day statuses—like inventory, employee headcounts, or daily stock values—require distinct handling compared to aggregatable metrics like sales or profit. Displaying the value on the last calendar day of a month holds significance in such cases. Moreover, transitioning from a monthly to a weekly view should dynamically update to showcase the last day of the week. For instance, in the stock data example below, assessing multiple ticker values at a daily level compares the average daily close value against the close value on the final day of the period. Employing a straightforward LOD Expression enables diving into daily granularity even within a visual display at a higher level of aggregation.

The following 6 examples illustrate how level of detail expressions can be applied to more advanced scenarios:

Evaluating the return purchases among customers holds significance, especially considering the costliness of acquiring new customers. Understanding the patterns of customers making repeat purchases within varying quarters—whether it's the first, second, third, or beyond—is essential. Additionally, assessing the count of customers who have never made a repeat purchase contributes valuable insights. This analysis, segmented by quarterly cohorts, sheds light on customer behavior over time.
Leveraging a FIXED Expression becomes instrumental in identifying each customer's first and second purchase dates, enabling the derivation of the time span in quarters for a repeat purchase. This nuanced approach offers a comprehensive understanding of customer return behavior within distinct quarterly cohorts.

Percent Difference from Average Across a Range

While Example 6 highlights comparing against a single selected item, what if the aim is to assess comparisons across a spectrum of values? Consider a scenario where one desires to evaluate the daily close value of a stock against the average daily close value before a significant industry-impacting event occurs.
In such instances, examining the percent difference becomes essential. By comparing the daily stock close values against the pre-event average, insights into the magnitude and impact of the event on stock performance can be gleaned. This analysis offers a broader perspective, aiding in understanding the deviation from the average within the context of industry-wide fluctuations.

Relative period filtering

When analyzing performance through year-to-date (YTD) and month-to-date (MTD) comparisons relative to the previous year, filtering relative to today is straightforward. However, when data undergoes weekly refreshes, discrepancies can arise. For instance, if the last refresh was on March 1 but the current day is March 7, a month-to-date comparison might inadvertently compare March 1 through March 7 of the previous year against March 1 of the current year, potentially causing unwarranted concern.
Employing a simple LOD (Level of Detail) Expression resolves this issue by identifying the maximum date within the dataset. This approach ensures accurate time-based comparisons, preventing misleading contrasts between different periods and providing a more precise evaluation of performance trends.

User login frequency

Understanding user login frequency is pivotal for assessing user engagement on websites or applications. This analysis aims to segment users based on their login frequency—whether it's monthly, bi-monthly, quarterly, and so on—and derive insights regarding the average login rate and its distribution around this average.
The dataset's granularity involves a log-in date per user ID, implying a row for each day a user accesses the platform. Slicing the number of customers by their login rate entails a more intricate analysis, necessitating the slicing of one measure by another measure. As showcased in Example 1, leveraging LOD (Level of Detail) Expressions streamlines this analysis, enabling an easy breakdown of user cohorts based on their login frequency and facilitating a comprehensive understanding of user behavior.

Proportional Brushing

In the realm of analysis, the pivotal question often revolves around comparison—specifically, "Compared to what?" Proportional brushing introduces a valuable technique for filtering where the aim is not merely to narrow down to the selection but to compare the selection against the total context.
This approach allows for a more comprehensive analysis by providing insights into how the selected subset relates to the entirety of the dataset. Proportional brushing aids in understanding the significance and impact of the chosen subset within the broader context, offering a richer perspective for informed decision-making.

Examining the correlation between customer tenure, measured by the year of acquisition, and loyalty, gauged through annual purchase frequency, provides valuable insights into customer behavior.
While Example 1 illustrates customers purchasing a specific number of times, marketers often seek insights beyond exact counts—particularly identifying customers who purchased at least a certain number of times. Moreover, understanding the loyalty trends within different acquisition cohorts is crucial. Simply assessing absolute customer numbers across cohorts might not reveal nuanced insights. Therefore, a more insightful approach involves evaluating the percentage of total customers within each cohort based on their purchase frequency thresholds.
In essence, this analysis combines variations of the number of orders LOD Expression, cohort Expression, and percent-of-total Expression to determine what percentage of customers within each cohort made at least one, two, three, or more purchases in a year. This approach offers a comprehensive understanding of loyalty trends across different customer acquisition periods.

Beinex Consulting has been awarded as the Alteryx 2020 Partner of the Year, Middle East at the Alteryx Partner Summit and Awards virtual event along with 15 other winners from North America, LATAM, and EMEA. During the Alteryx Summit, ‘Your Road to Revenue’, Alteryx celebrated the achievements and commitment of their partners to the Alteryx business and its customers. Beinex Consulting was awarded on the level of engagement in the Alteryx partner program and its efforts around driving innovation, growing revenue, and empowering Alteryx customers to solve our world’s most pressing business and societal issues in the Middle East Region. Selected among top Middle East Alteryx partners, Beinex demonstrated excellence in delivering end-to-end analytics transformation services that revolutionised multiple industries in the Middle East. Beinex Consulting Founder and Managing Director, Indumon Das indicates further growth for the digital transformation organisation soon: “Beinex continues to make strategic investments to enhance our association with Alteryx and clients in major Middle East markets. This award is a recognition to our continuous growth strategy and focus to be the best Middle East partner” “Through their ongoing pledge to the Alteryx Partner Program, our partners have demonstrated their commitment to helping Alteryx customers break down barriers and deliver game-changing insights.” – Josh Lewis, VP, Global Channels, Alteryx About Beinex Consulting Beinex is a digital transformation organization with a broad range of analytics modernization and training services. As a pioneer in analytics and cloud transformation, Beinex’s mission is to transform the way individuals and the organizations work with the data through innovation and experience. Beinex offers a broad range of robust and scalable business intelligence and analytics services to drive effective decision-making and create business value.

We are thrilled to recognize Beinex Consulting for being named Alteryx Middle East Partner of the Year!https://t.co/xwmp7HbsMp#TogetherWeSolve pic.twitter.com/4zic9mdlgD

— Alteryx (@alteryx) October 1, 2020

What is Data Governance?

Data governance is the process of managing the availability, usability, integrity, and security of data in an enterprise system. It establishes policies, procedures, and standards for how data is collected, stored, used, and shared. It's about ensuring the right data is available to the right people at the right time, in a secure and compliant manner.

Why Automate Data Governance?

Automating data governance processes brings several benefits, including: • Improved Data Quality: Automation can identify and correct data errors, inconsistencies, and duplicates, leading to improved data quality and more reliable insights. • Increased Efficiency: Automation streamlines data governance processes, reducing manual effort, freeing up valuable time, and accelerating decision-making. • Reduced Costs: Automation helps reduce the costs associated with manual data governance processes, minimizing errors and the need for rework. • Enhanced Compliance: Automation helps ensure compliance with data privacy regulations, such as GDPR, CCPA, and HIPAA, minimizing risk and potential penalties. • Better Decision-Making: By improving data quality and accessibility, automation enables better, more data-driven decision-making. • Scalability: Manual processes struggle to keep up with growing data volumes. Automation allows your governance framework to scale effectively.

How to Automate Data Governance Processes?

Several approaches exist for automating data governance, and often a combination is most effective: • Data Discovery and Classification: Automated tools, like data catalogs, can help discover and classify data based on its content, sensitivity, and other criteria. This is the foundation for understanding your data landscape. • Data Quality Monitoring: Automated tools can continuously monitor data quality, identify potential issues, and trigger alerts for remediation. • Data Lineage Tracking: Automated tools can track the origin and movement of data, providing a clear audit trail and helping to ensure data quality and compliance. This is crucial for understanding how data is transformed and used. • Data Access Control: Automated tools can enforce data access policies, ensuring that only authorized users can access sensitive data, protecting privacy and security. • Data Masking and Anonymization: Automated tools can mask or anonymize sensitive data to protect privacy while still allowing for data analysis and testing. • Metadata Management: Automated tools can capture and manage metadata, providing context and meaning to data assets, making them easier to find and understand.

The Role of Data Catalogs like Alation

Data catalogs play a critical role in automating data governance. They act as a central inventory of all data assets, providing a single source of truth about your data. Modern data catalogs like Alation offer: • Automated Data Discovery and Profiling: Automatically scan and profile data sources to identify and catalog data assets. • Data Lineage: Visually map the journey of data from its origin to its consumption, showing transformations and dependencies. • Data Quality Rules and Monitoring: Define and enforce data quality rules and automatically monitor data for compliance. • Collaboration and Knowledge Sharing: Enable data users to collaborate, share knowledge about data assets, and contribute to data governance efforts. • Search and Discovery: Empower users to easily find the data they need, along with relevant metadata and context.

Best Practices for Automating Data Governance

• Develop a Data Governance Strategy: Before automating, define clear goals, objectives, and metrics for your data governance program. • Identify Key Stakeholders: Engage business users, IT, compliance, and other stakeholders to ensure alignment and buy-in. • Choose the Right Tools: Select tools that meet your specific needs and integrate with your existing data infrastructure. Consider a platform approach that can address multiple aspects of governance. • Implement a Phased Approach: Start with a pilot project to demonstrate value and refine your approach before scaling to the entire organization. • Focus on Data Literacy: Train your employees on data governance policies, procedures, and the use of automated tools. • Monitor and Evaluate: Continuously monitor the effectiveness of your automated data governance processes and make adjustments as needed.

How to Embrace Better Data Governance?

Data governance doesn’t have to be a bottleneck. Organizations can reduce manual workloads, improve compliance, and drive innovation by adopting automation and leveraging tools like data catalogs. Ready to transform your data governance strategy? Get a Free Assessment Now!

Organisations take advantage of advanced analytics using the techniques given below:

Data Mining

Data mining is extracting useful information from large, raw chunks of data to find trends, plan new business strategies, increase revenue, decrease costs, reduce risks and enhance customer relationships. It establishes relationships and finds patterns and correlations to detect dangers and frauds and to make a profit out of businesses.

Sentiment Analysis (Opinion Mining)

Sentimental Analysis technique is used by businesses to detect emotion or feelings in textual data. It categorises the tone of writing as positive, negative or neutral. Organisations are benefitted in many ways by aiding in crisis prevention and understanding and analysing customers' opinions about their particular products or services. The companies monitor online conversations to learn about the customers' tastes, needs, and expectations.

Sentiment analysis' fully automated tools assist businesses in extracting information from unstructured and unorganised material found on the internet, such as blog posts, email, webchats, social media channels, and comments.

Cluster Analysis

It's a popular data-mining technique that matches unstructured data fragments based on commonalities discovered between them. Cluster analysis is instrumental for companies to identify different consumer groups and sales transactions or detect fraud. It is used in Machine Learning, image analysis, pattern recognition, information retrieval, data compression, bioinformatics and computer graphics.

Cluster analysis is a powerful data-mining tool for any company that wants to recognise discrete groupings of consumers, sales transactions, or other types of behaviours and things. Insurance firms use cluster analysis to identify fraudulent claims, and banks use it for credit scoring.

Retention Analysis

Studying user analytics to determine how and why consumers churn is known as retention analysis (or survival analysis). Retention analysis is crucial for learning how to keep a lucrative client base by increasing retention and new user acquisition.

Customer retention is a crucial practice in every business; companies can quickly decrease churn rates and increase customer satisfaction by tracking and taking advantage of customer behaviour.

Complex Event Analysis

Complex data analytics is the application of complex algorithmic approaches to effectively process huge unstructured data volumes. Computers perform data analysis; this was done mainly by individual machines acting on well-defined data structures in the past. This method uses technology to forecast high-level occurrences that are likely to occur due to a series of low-level factors.

Predictive Analysis

Predictive analysis is a technique used to analyse data and forecast the possibility of an event occurring in the future, allowing businesses to plan. It uses historical data combined with statistical modelling, data mining techniques and Machine Learning to predict risks and opportunities. Predictive analysis uses a scientific approach to forecast the future with a high degree of accuracy.

Machine Learning

Machine Learning is a crucial part of the AI subset of advanced analytics. This advanced analytic tool uses computational approaches to find patterns in data. It then uses them to build statistical models that can produce solid results without human participation. It falls into the following categories:

Supervised learning: The more common type of Machine Learning is supervised learning, which uses labelled data sets to allow you to search for specific patterns in the data. It requires vast datasets for the process; the more the amount of data, the more chances of getting accurate results.

Unsupervised learning: It employs various methods to find patterns and correlations in a subset of data. On the other hand, these algorithms are unable to recognise specific data sets, but they sort the information based on similarities and anomalies. However, it is applied in cybersecurity to find patterns from data.

Semi-supervised learning: It combines the benefits of supervised and unsupervised learning approaches. This technique uses unlabelled and labelled data to help the systems understand the challenge. The labelled data set is then utilised to aid in the model's training, with the results being used to mark the remaining unlabelled data. When all of the data has been labelled, the model is trained on it.

Reinforced learning: A relatively new advancement in Machine Learning, a reinforcement learning algorithm learns and develops to achieve a specific goal through trial and error. It tries out numerous choices before using rewards or penalties to help it make the best decision to achieve the goal.

Data Visualisation

Data representation in a visual or graphical style is known as data visualisation. It allows decision-makers to see analytics visually, making it easier to grasp complex topics or spot new patterns. Data visualisation aids in telling tales by transforming data into a more understandable format and showing trends and observations. A good visualisation tells a story by reducing noise from data and emphasising the essential facts. The common types of data visualisation include charts, tables, graphs, maps, infographics and dashboards.

Cohort analysis

Cohort analysis is employed to analyse the data and group it based on shared user behaviours during a specific period. It is a beneficial technique for boosting customer retention and happiness. By analysing behavioural patterns, it is possible to gain valuable information about what type of campaign is most likely to be successful, which customer group is most likely to buy your goods, and their expectations from a product. Cohort analysis can bring several advantages to a company:

Increased Customer Lifetime Value (CLV): Cohort analysis' capacity to assist a firm in improving client retention improves the CLV, which is the total money a business generates from a customer throughout their relationship.

Stronger relationships with loyal customers: Cohort analysis helps you discover your most loyal customers, allowing you to target them more precisely and encourage them to stay with you for as long as possible.

Better testing of new designs: In most cases, tests cannot predict how well a new design of a product will perform in the market. With the aid of cohort analysis, generate a cohort based on interactions with the latest design and compare it to the conversion rate of those that haven't.

Regression Analysis:

It is a powerful statistical method used to estimate the link between dependent (outcome) and independent (features) variables. The goal of regression analysis is to figure out how one or more factors may influence the dependent variable to spot trends and patterns. It is crucial for projecting future trends and generating forecasts.

To perform a regression analysis, you must first establish a dependent variable that you believe is influenced by one or more independent factors. After that, you'll need to create a comprehensive dataset to work with. Using surveys to get data from your target consumers is a great way to get started. All of the independent variables you are interested in should be addressed in your survey.

Different sectors like banking, insurance, retail, pharmacy, e-commerce and others used regression techniques to yield valuable, actionable business insights.

Advanced Analytics gives companies a greater understanding of data patterns and behaviour, allowing them to forecast future actions. It provides a substantial strategic advantage by revealing new business prospects and potential innovations, a deep awareness of customer and employee behaviours, fresh ways of looking at existing problems, and operational improvement opportunities, increasing revenue or lowering costs.Advanced Analytics analyses information from various data sources using predictive modelling, Machine Learning, and business process automation.

A Compact List of Snowflake Features

1. Decoupling of storage and compute in Snowflake
2. Auto-Resume, Auto-Suspend, Auto-Scale
3. Workload Separation and Concurrency
4. Snowflake Administration
5. Cloud Agnostic
6. Semi-structured Data Storage
7. Data Exchange
8. Time Travel
9. Cloning
10. Snowpark
11. Snowsight
12. Security Features
13. Snowflake Pricing

Let’s deep-dive:

1. Decoupling of storage and compute in Snowflake

Snowflake's decoupling of storage and compute features facilitates virtual warehouses and storage as separate entities. Leveraging this functionality of Snowflake, businesses can achieve greater flexibility in choosing the compute of their choice and incrementally pay for what they store and compute. Users can scale up / down or in/out based on the business SLA requirement. Scale up – Scale-out features do not require downtime and are almost instant.

2. Auto-Resume, Auto-Suspend, Auto-Scale

Snowflake's auto-resume and auto-suspend features provide minimal administration. Using auto-resume, Snowflake starts a compute cluster when a query is triggered and suspends compute clusters after a set time of inactivity. These two features ensure performance optimisation, cost management, and flexibility.

In business circumstances where more users are querying heterogeneous queries, setting up auto-scaling can help automatically expand the number of clusters from 1 to 10 at an increment of 1 based on the volume of queries sent to a compute simultaneously.

3. Workload Separation and Concurrency

Concurrency is no longer a problem for Snowflake, unlike traditional data warehouses with concurrency issues where users and processes must compete for resources. Because of Snowflake's multi-cluster architecture, concurrency is not an issue anymore.

This architecture also helps to divide workloads into their virtual warehouse and channel the traffic to each virtual warehouse (compute) by functions or departments.

4. Snowflake Administration

A data cloud as a service is provided by Snowflake (DWaas). Businesses can set up and administer a system without significant assistance from DBA or IT teams. Unlike the on-premise platforms, neither hardware commissioning nor software installation patch-update is s necessary. Snowflake manages software updates and introduces new functions and patches without downtime.

Snowflake automatically creates micro-partitioning. This feature reduces the requirement of manually indexing and clustering tables though these are available features in Snowflake.

5. Cloud Agnostic

Being a cloud-agnostic platform, Snowflake can migrate its workloads with other cloud providers. So, Snowflake is accessible on all three cloud providers: AWS, Azure, and GCP. Customers can easily integrate Snowflake into their existing cloud architecture and choose to deploy in locations preferred by their companies.

6. Semi-structured Data Storage

The requirement to manage semi-structured data, often in JSON format, gave rise to NoSQL database solutions. Data pipelines are created to extract attributes from JSON and mix them with structured data. By leveraging VARIANT, a schema on read data type, Snowflake's design enables storing structured and semi-structured data in the exact location. Both organised and semi-structured data can be stored using the VARIANT data type. Snowflake eliminates the need for data extraction pipelines by automatically analysing data, extracting properties, and saving it in a columnar format.

Snowflake can connect to staging areas like s3 bucket, Azure blob or GCP blob storage to retrieve and transform files stored in these platforms. This is regardless of the cloud Snowflake is hosted. A snowflake-managed staging area is also available. Tasks/ Streams or Snowpipe can be set to retrieve data at a scheduled time or almost instantly, respectively. Snowflake can work with CSV, JSON, XML, Avro, ORC, and Parquet file formats. Snowflake can also store metadata of unstructured data stored in the staging area.

7. Data Exchange

A wide range of data, data services, and applications are available on the Marketplace. From some of the world's top data and solution suppliers, you can find, assess, and buy data, data services, and apps through Marketplace. Direct access to data ready for querying and pre-built SaaS connections virtually eliminates the expenses and delays associated with conventional ETL operations and integration. The risk and hassle of duplicating and relocating outdated material should be avoided. Instead, you can receive automatic updates that are close to real-time and have secure access to shared, controlled, and live data.

8. Time Travel

One of the distinctive Snowflake elements is time travel. You may follow the evolution of data through time by using time travel. All accounts have access to this Snowflake feature, free and enabled by default for everyone. Additionally, this Snowflake feature allows you to retrieve a Table's historical data. At any moment throughout the previous 90 days, one can access the table's appearance.

Time travel encompasses the undrop feature. If an object has not been removed yet by the system, a dropped object can be recovered using the undrop command in Snowflake. When an object is undropped, it returns to its original condition. The option to undrop schemas or tables is also available.

9. Cloning

The clone capability allows us to quickly duplicate anything, including databases, schemas, tables, and other Snowflake objects, in almost real time. Therefore, cloning an object involves editing its metadata rather than duplicating its storage contents. You can quickly produce a clone of the whole production database for testing purposes.

10. Snowpark

With the help of the Snowpark feature, data scientists and data engineers proficient in Python, Scala, R, and Java may create and manage their codes in Snowflake. Snowpark helps to employ the computing capabilities of Snowflake to retrieve, transform, train and apply data science models on the data stored in Snowflake, which has a more apparent performance advantage,

11. Snowsight

The new Snowflake web user interface, Snowsight, replaces the traditional Snowflake SQL Worksheet and enables you to easily construct basic charts and dashboards that can be shared or explored by many users, do data validation while loading data and conduct ad-hoc data analysis. The Snowflake dashboards tool is an excellent option because it works well for individuals or small group users in an organisation who wish to generate straightforward visualisations and share information among themselves.

12. Security Features

Snowflake assures security for its users through the following methods:

• • By adding IP addresses to a whitelist, you may control network policies and limit who can access your account.
• • By supporting several authentication techniques, including federated authentication and two-factor authentication for SSO.
• • Using a hybrid approach of role-based access control and discretionary access control. In role-based access control, privileges are assigned to roles which are then transferred to users. Still, in discretionary access control, each object in the account has an owner who controls access to the object. This hybrid strategy offers a substantial level of flexibility and control.

AES 256 strong encryption is used to automatically encrypt all data, both in transit and at rest.

13. Snowflake Pricing

The advantages of Snowflake pricing are:

• • Pay for actual consumption only.
• • We can cut back on resource use to save costs.
• • Flexible payment. We can either pay on-demand or in advance (pre-purchased).
• • Scale up or down the use of cloud services, computing, and data storage automatically based on your needs.
• • There are no chances of overbuying or overprovisioning.

Optimisation of Snowflake spending through integration with innovative cost-monitoring platforms.

Snowflake stands as an ideal and popular choice because of its unique and updated features. It is also available across many data cloud providers and regions, making it accessible and suitable for all organisations. Why wait? Let’s experience Snowflake. Try now: https://beinex.com/snowflake/.