A look into Snowflake Data Types

A look into Snowflake Data Types

Reading Time: 9 minutes


As a Database as a Service (DBaaS), Snowflake is a relational Cloud Data Warehouse that can be accessed online. This Data Warehouse can give your company more leeway to adapt to shifting market conditions and grow as needed. Its Cloud Storage is powerful enough to accommodate endless volumes of both structured and semi-structured data. As a result, information from numerous sources can be combined. In addition, the Snowflake Data Warehouse will prevent your company from needing to buy extra hardware.

Snowflake allows you to use the usual SQL data types in your columns, local variables, expressions, and parameters (with certain limitations). An identifier and data type will be assigned to each column in a table. The data type tells Snowflake how much space to set aside for a column’s storage and what form the data must take.

Snowflake’s great global success can be attributed to the following characteristics: 

    • Snowflake’s scalability stems from the fact that it provides storage facilities independent of its computation facilities. Data is stored in a database and processed in a virtual data warehouse. As a result, Snowflake guarantees excellent scalability at a low cost.
    • Snowflake requires little upkeep because it was made with the user in mind. It has a low barrier to entry and needs little in the way of upkeep.
    • Automated query optimization is supported in Snowflake, saving you time and effort over the hassle of improving queries manually.
    • Snowflake allows you to divide your company’s regular workloads into different virtual Data Warehouses. As a result, this facilitates Data Analytics management, particularly under extremely regular loads.

Six Important Snowflake Data Types

The first step in becoming a Snowflake Data Warehouse expert is learning the ins and outs of the different types of data it stores. There are 6 different kinds of data that can be used with Snowflake.

    1. Numeric Data Types
    2. String & Binary Data Types
    3. Logical Data Types
    4. Date & Time Data Types
    5. Semi-structured Data Types
    6. Geospatial Data Types

1) Numeric Data Types

Knowing what precision and scale are is crucial before diving into the various sorts of numeric data types. 

    • A number’s precision is the maximum number of significant digits that can be included in the number itself.
    • Scale is the maximum number of digits that can be displayed following a decimal point.

Precision has no effect on storage; for example, the same number in columns with different precisions, such as NUMBER(5,0) and NUMBER(25,0), will have the same storage requirements. However, the scale has an effect on storage; for example, the same data saved in a column of type NUMBER(20,5) requires more space than NUMBER(20,0). Additionally, processing bigger scale values may take a little more time and space in memory.

So here are a few types of numeric data types:

    • NUMBER is a data type for storing whole numbers. The default scale and precision settings are 0 and 38, respectively.
    • DECIMAL and NUMERIC are the same as NUMBER.
    • The prefixes INT, INTEGER, BIGINT, and SMALLINT all mean the same thing as NUMBER. But you can’t change the scale or precision; these serial data types are permanently stuck at 0 and 38.
    • Snowflake uses double-precision IEEE 754 floating-point values (FLOAT, FLOAT4, FLOAT8). 
    • FLOAT is a synonym for DOUBLE, DOUBLE PRECISION, and REAL.
    • Numeric Constants are numbers that have fixed values. It supports the following format:

2) String & Binary Data Types

The following character-related data types are supported in Snowflake:

    • With a maximum size of 16 MB, VARCHAR can store Unicode characters of any size. There are BI/ETL tools that can set the maximum allowed length of VARCHAR data before storing or retrieving it.
    • CHARACTER, CHAR is like  VARCHAR, but with the default length as VARCHAR(1).
    • If you’re familiar with VARCHAR, you’ll feel right at home with STRING.
    • Just like VARCHAR, TEXT can store any kind of character.
    • The BINARY data type does not understand Unicode characters; hence its size is always expressed in bytes rather than characters. There’s an upper limit of 8 MB.
    • To put it simply, VARBINARY is another name for BINARY.
    • String Constants are fixed values. When using Snowflake, string constants must always be separated by delimiter characters. Delimiting string literals in Snowflake can be done with either single quotes or dollar signs.

3) Logical Data Types

In logical data type, you can only use BOOLEAN with one of two values: TRUE or FALSE. Sometimes it will show up as NULL if the value is unknown. The BOOLEAN data type offers the necessary Ternary Logic functionality.

SQL requires using a ternary logic, often known as three-valued logic (3VL), which has three possible truth values (TRUE, FALSE, and UNKNOWN). To indicate the unknown value in Snowflake, NULL is used. The outcomes of logical operations like AND, OR, and NOT are affected by ternary logic when applied to the evaluation of Boolean expressions and predicates.

    • UNKNOWN values are interpreted as NULL when used in expressions (like a SELECT list).
    • Use of UNKNOWN as a predicate (in a WHERE clause, for example) always returns FALSE

4) Date & Time Data Types

This details the date/time and time data types that can be managed in Snowflake. It also explains the allowed formats for string constants to manipulate dates, times, and timestamps.

    • The DATE data type is supported in Snowflake (with no time elements). It supports the most typical dates format (YYYY-MM-DD, DD-MON-YYYY, etc.).
    • DATETIME is shorthand for TIMESTAMP NTZ.
    • A TIME data type represented as HH:MM: SS is supported by Snowflake. Additionally, a precision setting for fractional seconds is available. The default precision is 9. The valid range for All-TIME values is between 00:00:00 to 23:59:59.999999999. 
    • An alternative name for any of the TIMESTAMP_* functions is TIMESTAMP, which can be set by the user. The TIMESTAMP_* variant is used in place of TIMESTAMP whenever possible. This data type is not stored in tables.
    • Snowflake supports three different timestamp formats: TIMESTAMP LTZ, TIMESTAMP NTZ, and TIMESTAMP TZ.


      • The TIMESTAMP LTZ function accurately records UTC. The TIMEZONE session parameter determines the time zone in which each operation is executed.
      • TIMESTAMP NTZ accurately records wallclock time. Without regard to local time, all tasks are carried out.
      • By default, TIMESTAMP TZ stores UTC time plus the appropriate time zone offset. The session time zone offset will be utilized if the time zone is not specified.

5) Semi-Structured Data Types

Semi-structured data formats, such as JSON, Avro, ORC, Parquet, or XML, stand in for free-form data structures and are used to load and process data. To maximize performance and efficiency, Snowflake stores these in a compressed columnar binary representation internally.

    • VARIANT is a generic data type that can hold information of any other type, including OBJECT and ARRAY. Its 16 MB of storage space makes it perfect for archiving large files.
    • OBJECT comes in handy to save collections of key-value pairs, where the key is always a non-empty string and the value is always a VARIANT. Explicitly-typed objects are currently not supported in Snowflake.
    • Display both sparse and dense arrays of any size with ARRAY. The values are of the VARIANT type, and indices can be any positive integer up to 2^31-1. Arrays of a fixed size or containing values of a non-VARIANT type are not currently supported in Snowflake.

6) Geospatial Data Types

Snowflake has built-in support for geographic elements like points, lines, and polygons. The GEOGRAPHY data type, which Snowflake provides, treats Earth as though it were a perfect sphere. It is aligned with WGS 84 standards.

Degrees of longitude (from -180 to +180) and latitude (from -90 to +90) are used to locate points on Earth’s surface. As of right now, altitude is not a supported option.  More so, Snowflake provides GEOGRAPHY data-type-specific geographic functions.

Instead of retaining geographical data in their native formats in VARCHAR, VARIANT, or NUMBER columns, you should transform and save this data in GEOGRAPHY columns. The efficiency of geographical queries can be greatly enhanced if data is stored in GEOGRAPHY columns.

The following geospatial objects are compatible with the GEOGRAPHY data type:

    • Point
    • MultiPoint
    • MultiLineString
    • LineString
    • GeometryCollection
    • Polygon
    • MultiPolygon
    • Feature
    • FeatureCollection

Unsupported Data Types

If the above list of SQL server data types is clear, then what is the type of data that is incompatible with Snowflake? Here is your answer.

  • LOB (Large Object) 
    • BLOB: You can also utilize BINARY, with a maximum size of 8,388,608 bytes. 
    • CLOB: You can also use VARCHAR, with a maximum size of 16,777,216 bytes (for a single byte).
  • Other
    • ENUM
    • User-defined data types


While your primary focus should be on learning how to use customer data, you may be questioning why it’s necessary to know so many different data types. There is one motive for doing this, and that is to amass reliable information. Data collection and instrumentation aren’t the only areas where you can use your data type knowledge; you’ll also find that data administration, data integration, and developing internal applications are much less of a challenge now that you have a firm grasp on the topic.

Also, without a good database management system, it is impossible to deal with the massive amounts of data already in existence. Get in touch with our experts for more information.

How to Choose the Best Data Visualization Tools

How to Choose the Best Data Visualization Tools

Reading Time: 17 minutes


Data is getting immense with every passing year and in nearly all industries. As metrics pile up considerably, you, as an organizational decision-maker, may find yourself confused about which data points collected are essential and in what approaches they can assist your business operations. 

All of this data is tough for the human brain to grasp. It is tricky to comprehend numbers more significant than five for a human brain without sketching some abstraction. Data visualization professionals can play a vibrant role in generating those abstractions.

Big data is impractical if it can’t be understood and digested conveniently. That is why data visualization plays a significant role in the whole thing, from economics to technology, enabling decision-makers in IT companies and end users of BI technologies like hospitals and industries like manufacturing.

By converting multifaceted numbers and other pieces of data into visual elements, content becomes simpler to comprehend and use in diverse applications.

So, here, you require data visualization techniques and need to select the best tools that can maximize your utilities.

What is Data Visualisation?

Data visualization in simple terms is an arrangement of visual elements of a set of data that is highly interactive, intuitive, personalized, and easy to share. 
For instance, text-based data is visualized graphically in the outline of charts, graphs, tables, Infographics, and maps to analyze business or operational scenarios. 

So, by manipulating big data sets in the form of visual formats, you can clearly understand the story your data depicts at a swift glance, instead of working on piles of tables and numbers for long hours.

How does it Enable Business Intelligence Dynamics?

Now coming to the context of Business Intelligence (BI) dynamics, data visualization is used and applied in two ways. 

Data is visualized in form of Dashboards that represent business data from every angle by allowing one to measure its performance in any dimension. Data can be drilled down and dissected any information. We can slice & dice the information in any unit size.

Do you want to know what valuation Business Intelligence (BI) can bring to your organization?

Data Visualisation can Assist your Organisation with Diverse Approaches

How does data visualization help decipher digital information?

Large and ever-altering quantities of data related to your business’s health, such as customer interactions, user experiences, staff performance levels, and expenditures can robustly impact and influence the overall decision-making at crucial moments. However, this is only probable when such data is clearly understandable even by non-data professionals.

With data visualization, you can translate scores of text and numbers to instinctively understandable insights. A step further, visualization tools can transform raw metrics into insightful stories that can be easily shared and worked upon.

How can data visualization help discover trends swiftly?

Data visualization facilitates your organization to spot alterations in customer behavior and market conditions swiftly. For instance, by utilizing heat maps, one can rapidly spot expansion opportunities, which is not evident in spreadsheets. 

On the other hand, Radius maps enable you to focus on spatial relationships for realizing enhanced business efficiencies or oversupply.
Further, with territory mapping, your sales teams can easily view their territories and ensure they are aligned or not.

How does data visualization help with decision analysis?

When you feed precise and neutral data visualizations into the decision-making tools, you can make enhanced decisions for your organization. Accurate data visualizations don’t deform the original information with unreliable displays. 

Additionally, charts and dashboards should be updated with dynamism using the newest information keeping the decision-making analysis highly applicable and relevant.

How data visualization reveals flaws, fraud, and anomalies?

Erroneous data can lead to a severe threat to businesses that depend on their correctness and accuracy levels. Data visualizations like charts and graphs can swiftly highlight large discrepancies in data readings, specifically signaling, where more careful reviewing of the numbers may be crucial.

Identifying and visualizing data patterns

Data visualization software enables you to identify and visualize data patterns with relationships that occur amid daily operations and overall business performance. 

However, you should be cautious of inappropriate comparative visualizations as if your organizational data analysis is puzzling or tough to compare; your visualizations might be doing more damage than enhancements.

Following are two charts that illustrate: 
a) Poor Data Visualisation,
b) Enhanced Data Visualisation through Dashboard.

a) Poor Data Visualisation: 

b) Enhanced Data Visualisation through Dashboard.

Let us further explore the bad data visualization and good data visualization examples in detail.

Example of Bad Data Visualization 

#1: Pie chart with multiple categories

bad data pie chart

Pie charts are leveraged when 2 to 3 product items make up the complete data set. Any more than that, and it is tough for the human eye to differentiate between the parts of a circle.

Notice how difficult it is to differentiate the size of these diverse parts. 

What is the exact difference between India and Russia?

It is rough to calculate the exact size difference. Rather, substitute this with a bar chart.

Example of Good Data Visualization: Precise Bar Chart

good data bar chart

Here you can explicitly calculate the difference between India (6.80%) and Russia (4.90%).

Bar charts will be your go-to option for exact data visualization.

7 Best Data Visualization Tools Which Are Popular In 2022-23

1. Power BI

Power BI is effortless to set up with dashboards and data connectors to on-premise and cloud-based sources such as Salesforce, Azure SQL DB, or Dynamics 365. The open framework enables the creation of custom visuals. 

It possesses default data visualization elements with bar charts, pie charts, maps, and even complex models like waterfalls, funnels, gauges, and other components. 

Power BI is developed and enabled with machine learning abilities, so it can automatedly spot patterns in data using them to make informed predictions through “what if” scenarios. These estimates facilitate users to make forecasts and meet future demands or significant metrics. 

A user can easily save his work to a file, and publish data and reports through Power BI to share with other stakeholders. Power BI is utilized to develop custom dashboards as well as reports as per the relevancy and access of data. 

Through custom visuals SDK, one can generate stunning visualizations, based on rich JavaScript libraries like D3, jQuery, and R-language scripts.

You also might like to read more about our best case study which is Remodelling advertising pricing strategy with Data Analytics 


2. Tableau

Tableau has an extensive customer base of more than 57,000 accounts because of its capability to generate interactive visualizations far beyond those offered by standard BI solutions. 

It is best for managing massive and quickly altering datasets utilized in Big Data operations, machine learning, and artificial intelligence applications. Further, it can be integrated with modern database solutions including Amazon AWS, Hadoop, My SQL, Teradata, and SAP.

Developing content in Tableau doesn’t need conventional source control or dev-test-prod-related techniques. You can integrate Tableau content development and deployment into your present development systems.

Publishing data to Tableau is integral to sustaining a single source for accessible data. Publishing facilitates sharing data with colleagues; even those not using Tableau Desktop, however, have required editing permissions. 

The top features of Tableau include Tableau Dashboard, Collaboration and Sharing, Live and In-memory Data, Data Sources, Advanced Visualizations (Chart Types), Maps, Mobile view, and robust security. D3.js is an exclusive JavaScript library that is utilized for Tableau data visualization.

3. MicroStrategy

MicroStrategy provides intuitive tools with data discovery and big data analytics features with an extensive library to visualize data. 

The MicroStrategy platform backs engaging dashboards, scorecards, advanced reports, thresholds, alerts, and automated report distribution. The tool can connect to over 200 data sources which include RDBMS, Cloud data, OLAP, and Big data.

Dossiers are MicroStrategy’s advanced and modern dashboards. To make the dossier to be presentation-ready, one requires to certify it to validate that the content is trustworthy. Once certified, you can share it with the enterprise environment for collaboration and publishing.

MicroStrategy Library is a unique and personalized virtual bookshelf that enables you to access dossiers from one common location. Through the MicroStrategy library, you can reach out to subject matter specialists and have a conversation regarding your data visualizations.

4. Qliksense

The vendor has 40,000+ customer accounts across 100+ countries, offering a highly adaptable setup and extensive features. 

Along with its data visualization abilities, the Qliksense tool even provides business intelligence, and enables the storytelling capacity of dashboards, data analytics, and reporting with a sleek user interface. 

There is also a sturdy community and 3rd party resources obtainable online to assist fresh users in understanding how to incorporate it into their current projects.

The Qliksense dashboard is an influential feature to showcase values from multiple fields simultaneously, and its functionality of data association in memory can showcase the dynamic values in all the available sheet objects. 

Qlik DataMarket® is an integrated data-as-a-service (DaaS) of Qlikview offering an all-inclusive library of data sets from reliable sources. Qliksense developers can use the same and effortlessly enable their analyses with external data sets to have an “outside-in” perspective for deeper insights.

5. Google Data Studio

Google Data Studio is a tool that enables communication and acts on tailored data sets. Programmers, executives, and worldwide team members from diverse departments can match, filter, and well-organize the precise data sets they require swiftly in one single report. No more waiting for numerous and static data reports to fill their inbox.

Data Studio is now an integral part of Google Cloud’s BI solutions. By blending Data Studio with Looker, Google Cloud has the finest of both ends – a structured semantic model and a self-served, simple-to-use front-end app with Data Studio that enables the analysis of unstructured/ungoverned data sets.

6. Apache Superset

Apache Superset is an advanced exploration and data visualization platform. It can substitute or enhance proprietary BI tools for many teams. It blends well with a diversity of data sources.

It offers a no-code interface for swiftly crafting charts. It provides a powerful web SQL Editor for progressive querying and a lightweight semantic layer for rapidly defining custom dimensions and precise metrics.

It provides an extensive array of attractive visualizations to display your data sets, ranging from straightforward bar charts to geospatial visualizations.

7. Looker

Looker Studio is a self-service BI with unmatched suppleness for intelligent business decisions. It helps tell powerful stories by building and sharing interactive reports and data visualizations. 

It assists in transforming your data sets to business metrics and dimensions with intuitive, intelligent reports. The tool enables professionals with significant business metrics by sharing automated dashboards. It helps you generate shareable, tailored charts and graphs with merely a few clicks.

Moving Forward

Extract, transform & load (ETL) are 3 data processes, enabled after data collection. 

Extraction takes data, collected in varied data sources with diverse structures and formats, to the staging database. 

Transformation takes fetched data and applies predefined rules to it, and load takes the transformed data and stores it in Data Warehouse (DW). 

However, this data is multifaceted until it is parsed and showcased in a simplified way. 

Specialists at Data Nectar enable the seamless consumption of significant insights by transforming the data analysis into visual representations with the assistance of Reports and Dashboards to decipher trends, anomalies, and data usage patterns.

At Data Nectar, a data analytics and visualization technology company, we know the real significance of Data Visualization for multiple stakeholders, and we can assist you in choosing precise tools in line with your requirements. 

Further, we enable SMEs and Enterprises with analytics-driven technology solutions to realize enhanced performance and maximize ROI in the process – through data. 

If you all too, as your organization’s decision-makers are willing to discover the vast possibilities Data can bring to your business or industry operations, Call Us Today!

Azure Analytics – Timely insight for Data-driven decisions

Azure Analytics – Timely insight for Data-driven decisions

Reading Time: 4 minutes


A data-driven culture is critical for businesses to thrive in today’s environment. In fact, a brand-new Harvard Business Review Analytic Services survey found that companies who embrace a data-driven culture experience a 4x improvement in revenue performance and better customer satisfaction.

Foundational to this culture is the ability to deliver timely insights to everyone in your organization across all your data. That is exactly what Microsoft aims to deliver with Azure Analytics and Power BI, and we should say that their cloud-first approach and efforts are paying off in value for customers. According to a recent commissioned Forrester Consulting Total Economic Impact™ study, Azure Analytics and Power BI deliver incredible value to customers with a 271 per cent ROI, while increasing satisfaction by 60 per cent.

Azure Analytics’ position in the leaders quadrant in Gartner’s 2019 Magic Quadrant for Analytics & BI, coupled with their performance in analytics could help businesses to have a strong foundation needed to implement a data-driven culture.

Basically, there are three key attributes needed to establish a data-driven culture

First, it is vital to get the best performance from your analytics solution across all your data, at the best possible price.

Second, it is critical that your data is accurate and trusted, with all the security and privacy rigour needed for today’s business environment.

Finally, a data-driven culture necessitates self-service tools that empower everyone in your organization to gain insights from your data.

Let’s take a deeper look into each one of these critical attributes.


When it comes to performance, Azure has it well covered. An independent study by GigaOm found that Azure SQL Data Warehouse is up to 14x faster and costs 94% less than other cloud providers. This unmatched performance is why leading companies like Anheuser-Busch Inbev adopt Azure.

Business can leverage the elasticity of SQL Data Warehouse to scale the instance up or down, so that customer only pays for the resources when they’re in use, significantly lowering our costs. This architecture performs significantly better than the legacy on-premises solutions and it also provides a single source of truth for all of the company’s data.


Azure is the most secure cloud for analytics. This is according to Donald Farmer, a well-respected thought leader in the data industry, who recently stated, “Azure SQL Data Warehouse platform offers by far the most comprehensive set of compliance and security capabilities of any cloud data warehouse provider”. Since then, Microsoft announced Dynamic Data Masking and Data Discovery and Classification to automatically help protect and obfuscate sensitive data on-the-fly to further enhance data security and privacy.


Only when everyone in your organization has access to timely insights can you achieve a truly data-driven culture. Companies drive results when they break down data silos and establish a shared context of their business based on trusted data. Customers that use Azure Analytics and Power BI do exactly that. According to the same Forrester study, customers stated.

“Azure Analytics has helped with a culture change at our company. We are expanding into other areas so that everyone can make informed business decisions.” -Study interviewee
“Power BI was a huge success. We’ve added 25,000 users organically in three years.” – -Study interviewee

Azure Analytics and Power BI together can unlock the performance, security and insights for your entire organization. Its matured technology and tools propositions enable you to develop a data-driven culture needed to thrive. customers like Reckitt Benckiser, choose Azure for their analytics needs.

“Data is most powerful when it’s accessible and understandable. With this Azure solution, our employees can query the data however they want versus being confined to the few rigid queries our previous system required. It’s very easy for them to use Power BI Pro to integrate new data sets to deliver enormous value. When you put BI solutions in the hands of your boots on the ground—your sales force, marketing managers, product managers—it delivers a huge impact to the business.”

Wilmer Peres, Information Services Director, Reckitt Benckiser

When you add it all up, Azure Analytics and Power BI offer strong data analytics capabilities and scalability for growing needs. To learn more about Azure’s insights for all advantage, let’s connect!