What is CDbl in SQL? Unraveling Double-Precision Conversion for Data Accuracy

I recall a particularly stressful Monday morning when Sarah, a brilliant data analyst I worked alongside, found herself staring at a spreadsheet that just didn’t add up. She was working on a critical report for our finance department, calculating complex interest rates and compounding growth over several years. The numbers were off, by just a tiny fraction, but enough to cause alarm. “It’s gotta be a data type issue,” she murmured, frustration etched on her face. “I’m pulling these values from the SQL database, and something’s losing precision on the way.” Sarah’s predicament is a classic example of why understanding data type conversions, especially for floating-point numbers, is absolutely vital in the world of SQL. And for those working in environments like Microsoft Access or VBA, the term that often pops up is CDbl.

So, what exactly is CDbl in SQL? Simply put, CDbl is a function primarily found in Microsoft Access SQL and VBA (Visual Basic for Applications) that converts an expression into a double-precision floating-point number. This data type is designed to handle decimal values with a much higher degree of precision and a wider range than single-precision floating-point numbers or integers. While CDbl itself isn’t a standard SQL function across all database systems, the concept it represents—converting data to a double-precision format—is crucial and accomplished using functions like CAST or CONVERT with the DOUBLE PRECISION or FLOAT data type in other SQL dialects like SQL Server, PostgreSQL, MySQL, and Oracle. It’s all about ensuring your numbers have the room they need to breathe and retain their accuracy, especially in calculations where even a tiny rounding error can snowball into a significant discrepancy.

The Bedrock of Numbers: Understanding SQL Data Types

Before we dive deeper into CDbl and its brethren, it’s essential to grasp the fundamental numeric data types that SQL databases offer. Think of these data types as different-sized containers for your numbers, each with its own capacity and characteristics. Choosing the right container is paramount for both storage efficiency and, more importantly, data accuracy.

• Integers (INT, TINYINT, SMALLINT, BIGINT): These are for whole numbers only—no decimal points allowed. They’re great for counts, IDs, or anything that can’t be split into fractions. They’re typically very efficient for storage and calculations.
• Fixed-Point Decimals (DECIMAL, NUMERIC): When you absolutely, positively need exact decimal precision, especially for financial data like currency, these are your go-to. You specify the total number of digits (precision) and the number of digits after the decimal point (scale). For example, DECIMAL(10, 2) can store numbers up to 9,999,999.99 with exactly two decimal places. The downside? They can be less efficient than floating-point numbers for certain types of computations.
• Floating-Point Numbers (FLOAT, REAL): These are designed to store approximate numeric values. They trade exactness for a wider range of values and often faster computation speeds.
  • REAL (Single-Precision): Typically uses 4 bytes of storage and offers about 7 decimal digits of precision. Think of it as a good general-purpose decimal number where absolute precision isn’t critical.
  • FLOAT (Double-Precision in some contexts, or generally a synonym for REAL with higher precision): In many SQL systems, FLOAT without a specified precision often defaults to double-precision. It typically uses 8 bytes of storage and provides about 15-17 decimal digits of precision. This is where the concept of CDbl truly lives.

The core challenge, as Sarah discovered, often arises when these different types interact. An integer divided by an integer might yield an integer result, truncating any decimal part. Or, a calculation involving `REAL` might not carry enough precision for complex scientific or financial models, leading to those subtle, yet significant, inaccuracies.

The Heart of the Matter: CDbl and Double Precision

Let’s home in on CDbl. As mentioned, CDbl is a specific function you’ll find primarily in the Microsoft ecosystem, particularly within Microsoft Access database queries and VBA code. Its purpose is singular: to take whatever value you give it—be it an integer, a string representing a number, or even another numeric type—and convert it into a double-precision floating-point number.

A double-precision floating-point number is a standard numerical data type that uses 64 bits (8 bytes) of computer memory to store a value. This substantial storage capacity allows it to represent a vast range of numbers, from extremely small to incredibly large, and crucially, with a significant number of decimal places—typically around 15 to 17 significant digits. This level of precision is often sufficient for most scientific, engineering, and statistical computations where the values are approximations or derived from measurements.

Why Go Double? The Imperative for Precision

So, why would you explicitly convert to double precision? Here are some compelling reasons:

1. Complex Calculations: When performing divisions, square roots, trigonometric functions, or other advanced mathematical operations, intermediate results can often have many decimal places. If you use a data type with insufficient precision (like an integer or even a single-precision float), these intermediate results will be rounded or truncated, leading to cumulative errors. Double precision minimizes these rounding errors.
2. Scientific and Engineering Data: Fields like physics, chemistry, engineering, and geospatial analysis frequently deal with measurements that require high precision. Latitude and longitude, for instance, are often stored and processed as double-precision numbers to ensure accurate location tracking.
3. Interoperability: When exchanging data with external systems, APIs, or programming languages (like Python, Java, or C#), double-precision floating-point numbers are a common standard for representing decimal values. Explicitly converting to this type ensures compatibility and prevents data misinterpretation.
4. Avoiding Implicit Conversion Pitfalls: SQL databases often perform implicit data type conversions when you mix types in expressions. While sometimes convenient, implicit conversions can be unpredictable and lead to unintended precision loss or errors. Explicitly using CDbl (or its equivalents) makes your intention clear and helps prevent these surprises.

My own experiences have taught me that relying on implicit conversions is like walking a tightrope without a net. You might get away with it most of the time, but the one time you don’t, it’s often in a critical production environment, and the fall is painful. Explicit conversions, even if they add a little verbosity, are a bedrock of robust SQL development.

CDbl in Microsoft Access SQL and VBA

For those living in the Microsoft Access or VBA world, CDbl is a native and frequently used function. It’s part of the comprehensive set of conversion functions VBA provides (CInt, CLng, CStr, etc.).

Syntax and Usage

The syntax for CDbl is straightforward:

CDbl(expression)

Where `expression` is any string or numeric value that you want to convert.

Practical Examples in Access SQL

Let’s imagine you have a table named `SalesData` with a column `UnitPrice` (stored as `Currency`, which is a fixed-point decimal type) and `Quantity` (stored as `Integer`). You want to calculate the `TotalRevenue` for an item, and then compute the average revenue per sale. If you simply perform the multiplication, Access might handle the precision reasonably well for currency, but if you then start doing more complex operations, or converting to other types, you might want explicit control.

Example 1: Basic Conversion

Suppose you have a `Variant` type or a string that you know represents a number, and you need to ensure it’s treated as a double-precision number for a calculation.

SELECT CDbl("123.45678912345") AS ConvertedValue;

This would return `123.45678912345` as a double-precision number.

Example 2: Ensuring Precision in Calculations

Let’s say `UnitPrice` is a `Currency` type, and `TaxRate` is a `Single` (single-precision float). If you want to ensure the `TotalTax` calculation maintains maximum precision, you might cast one or both to `CDbl`.

SELECT
ProductID,
UnitPrice,
TaxRate,
CDbl(UnitPrice) * CDbl(TaxRate) AS TotalTax
FROM
OrderItems;

In this scenario, `CDbl(UnitPrice)` and `CDbl(TaxRate)` explicitly tell Access to perform the multiplication using double-precision arithmetic, thus carrying more decimal places in the `TotalTax` result than if you relied on implicit conversion rules between `Currency` and `Single`.

Example 3: Aggregation with Precision

If you’re calculating an average of values that might be stored with less precision, converting them to `CDbl` before aggregation can help. Though `AVG` often promotes to a higher precision type anyway, explicit conversion adds robustness.

SELECT
AVG(CDbl(SalesAmount)) AS AverageSales
FROM
DailySales;

Here, `SalesAmount` might be `Currency` or `Single`. Converting it to `CDbl` before averaging ensures that the sum and count used for the average are performed with double-precision accuracy, potentially yielding a more precise average.

It’s worth noting that while CDbl is highly useful in Access, if you find yourself needing to move data or queries to other, more robust SQL database systems, you’ll need to adapt. This brings us to the crucial point: standard SQL doesn’t have a function named CDbl.

Standard SQL Equivalents to CDbl

For the vast majority of SQL database systems outside of Access/VBA, you won’t find a CDbl function. Instead, the standard SQL way to perform type conversions, including to double-precision floating-point numbers, is through the CAST and CONVERT functions, usually targeting the FLOAT or DOUBLE PRECISION data type.

Let’s look at how different major SQL dialects handle this.

SQL Server

SQL Server uses both CAST and CONVERT. For double-precision floating-point numbers, you’d typically use FLOAT (which defaults to 64-bit precision) or `FLOAT(n)` where `n` is 53 for 64-bit (double precision).

Syntax:

• CAST(expression AS FLOAT)
• CONVERT(FLOAT, expression)

Examples:

SELECT CAST('123.456789123456789' AS FLOAT) AS ConvertedValue;
SELECT CONVERT(FLOAT, '987.6543210987654321') AS ConvertedValue2;

If you have a `DECIMAL` column, say `CalculatedRatio DECIMAL(18, 10)`, and you need to perform further calculations that might benefit from `FLOAT`’s computational speed or range, you’d do:

SELECT
ProductID,
CAST(CalculatedRatio AS FLOAT) AS FloatingRatio
FROM
ProductMetrics;

PostgreSQL

PostgreSQL is quite flexible, supporting both standard CAST and a more concise typecast operator.

Syntax:

• CAST(expression AS DOUBLE PRECISION)
• expression::DOUBLE PRECISION (This is a PostgreSQL-specific shorthand)

Examples:

SELECT CAST('123.456789123456789' AS DOUBLE PRECISION) AS ConvertedValue;
SELECT '987.6543210987654321'::DOUBLE PRECISION AS ConvertedValue2;

For a column `SensorReading` stored as `NUMERIC(10, 4)`:

SELECT
ReadingID,
SensorReading::DOUBLE PRECISION AS DoubleSensorReading
FROM
SensorData;

MySQL

MySQL also uses CAST and CONVERT, with DOUBLE being the equivalent data type for double precision.

Syntax:

• CAST(expression AS DOUBLE)
• CONVERT(expression, DOUBLE)

Examples:

SELECT CAST('123.456789123456789' AS DOUBLE) AS ConvertedValue;
SELECT CONVERT('987.6543210987654321', DOUBLE) AS ConvertedValue2;

If you have a `DECIMAL` column `SharePrice`:

SELECT
StockSymbol,
CAST(SharePrice AS DOUBLE) AS DoubleSharePrice
FROM
StockQuotes;

Oracle

Oracle also uses CAST, often to its `BINARY_DOUBLE` data type, which is Oracle’s specific implementation of IEEE 754 double-precision floating-point numbers.

Syntax:

• CAST(expression AS BINARY_DOUBLE)

Examples:

SELECT CAST('123.456789123456789' AS BINARY_DOUBLE) AS ConvertedValue FROM DUAL;
SELECT CAST(12345.67890123456789 AS BINARY_DOUBLE) AS ConvertedValue2 FROM DUAL;

For a `NUMBER` column `Measurement`:

SELECT
DeviceID,
CAST(Measurement AS BINARY_DOUBLE) AS DoubleMeasurement
FROM
DeviceReadings;

Comparison Across SQL Dialects

To summarize the equivalents to CDbl across different SQL systems, here’s a handy table:

This table really highlights that while the function name changes, the core concept of converting to a high-precision floating-point number remains consistent across different database platforms. It’s not about memorizing CDbl, but understanding the intent behind it.

Diving Deeper: The Nature of Double-Precision Floating-Point Numbers

To truly master the use of `CDbl` (or its equivalents), we need to understand a bit about how these “double-precision floating-point numbers” actually work under the hood. They adhere to the IEEE 754 standard, which dictates how computers represent these numerical values using a fixed number of bits.

A 64-bit double-precision number is typically broken down into three parts:

1. Sign Bit: A single bit that indicates whether the number is positive or negative.
2. Exponent: Around 11 bits dedicated to storing the exponent, which determines the magnitude of the number (how large or small it is).
3. Significand (or Mantissa): Approximately 52 bits that store the significant digits of the number. This is where the “precision” comes from.

This binary representation allows for a massive range of numbers (from around 4.9e-324 to 1.8e+308), and crucial for Sarah’s calculations, a high number of significant digits (typically 15-17 decimal digits). However, and this is a critical distinction, these numbers are *approximate* representations of real numbers.

Precision vs. Accuracy: A Critical Distinction

This is where things can get a little tricky. `DOUBLE PRECISION` offers incredible precision—meaning it can store a value with many digits after the decimal point. But it doesn’t always guarantee perfect accuracy for exact decimal representations.

Consider the fraction 1/3. In decimal, it’s 0.3333… an infinite string of threes. We can’t represent it exactly with a finite number of decimal places. Similarly, many simple decimal fractions (like 0.1, or 1/10) cannot be represented *exactly* in binary floating-point format, just like 1/3 can’t be exactly represented in decimal. It’s like trying to perfectly express 1/3 using only powers of 10. You’ll always have a tiny remainder.

This means that while `CDbl` gives you a lot of decimal places, those last few places might be slightly off from the true mathematical value if the number itself doesn’t have an exact binary representation. This is not a flaw; it’s an inherent characteristic of the floating-point number system.

This is why, as a seasoned pro, I always preach caution: for values where *exact* decimal representation is paramount, such as financial ledger balances, currency amounts, or precise inventory counts, you should almost always lean towards `DECIMAL` or `NUMERIC` data types. These types store numbers as exact base-10 values, eliminating those tiny binary representation discrepancies.

Navigating the Treacherous Waters: Potential Pitfalls and Best Practices

While `CDbl` and its equivalents are powerful tools, wielding them effectively requires an understanding of their nuances. Here are some pitfalls to watch out for and best practices to adopt:

Pitfall 1: Misusing for Monetary Values

The Golden Rule: Never use floating-point types (FLOAT, REAL, DOUBLE PRECISION, or values obtained via CDbl) for storing or calculating currency amounts that require exact results. Those tiny binary representation errors can lead to real financial discrepancies over many transactions. Imagine a bank’s interest calculations being off by a minuscule amount on millions of accounts; it adds up to a huge problem.

• Best Practice: Always use DECIMAL or NUMERIC data types for financial data, defining both precision and scale (e.g., DECIMAL(19, 4) for currency up to four decimal places).

Pitfall 2: Direct Equality Comparisons

Due to the approximate nature of floating-point numbers, directly comparing them for equality (e.g., `WHERE Price = 10.50`) can lead to unexpected results. Two numbers that appear identical might have tiny differences at the very limits of their precision, making the equality check fail.

• Best Practice: When comparing floating-point numbers, instead of `A = B`, check if the absolute difference between them is less than a very small “epsilon” value: `WHERE ABS(A – B) < 0.0000001`. This allows for a small margin of error.

Pitfall 3: Data Loss During Conversion

Converting from a higher-precision fixed-point type (like `DECIMAL(38,10)`) to `DOUBLE PRECISION` can potentially lose precision if the `DOUBLE PRECISION` type cannot represent the value exactly or if it exceeds its range. Similarly, converting a string that contains a very large number of decimal places to `CDbl` will result in rounding to 15-17 significant digits.

• Best Practice: Always be aware of the target data type’s limitations. If you have extremely high-precision `DECIMAL` values, converting to `DOUBLE PRECISION` should be done only if that slight loss of precision is acceptable for your specific calculation or use case. Test your conversions with boundary values.

Pitfall 4: Implicit Conversions

Relying on the database to implicitly convert data types for you can sometimes lead to unexpected outcomes. For example, if you divide an integer column by another integer column, the database might perform integer division, truncating any decimal part, even if the result is stored in a `FLOAT` column.

• Best Practice: Make conversions explicit using `CDbl`, `CAST`, or `CONVERT`. This clearly communicates your intent and helps prevent the database from making assumptions you didn’t anticipate. For instance, `CAST(ColumnA AS DOUBLE) / ColumnB` ensures the division is performed using floating-point arithmetic.

Checklist: When to Reach for Double Precision (or its equivalent)

Here’s a quick mental checklist to help you decide if `CDbl` (or `CAST AS DOUBLE/FLOAT`) is the right choice:

• Do I need to store or compute values with many decimal places (more than 7-8)?
• Am I performing complex mathematical operations like division, square roots, or trigonometric functions?
• Is the data inherently approximate (e.g., scientific measurements, sensor readings, geospatial coordinates)?
• Am I interfacing with other systems or programming languages that expect standard 64-bit floating-point numbers?
• Is the minor loss of *exactness* for some decimal values acceptable (i.e., this isn’t currency)?
• Do I need a wide range of values, from extremely small to extremely large?

If you answered “yes” to most of these, then double-precision conversion is likely a good path.

When to Avoid Double Precision:

• When dealing with monetary values or financial ledgers.
• When exact decimal representation is non-negotiable (e.g., tax calculations that must match exactly).
• When comparing values for precise equality.
• When you only need a few decimal places and exactness is preferred (consider `DECIMAL` with a small scale).

Real-World Scenarios and Use Cases

Let’s paint a clearer picture of where double-precision numbers shine:

1. Scientific and Engineering Simulations: Imagine simulating fluid dynamics or celestial mechanics. The equations involve countless multiplications and divisions of values that have many significant figures. Using `DOUBLE PRECISION` ensures that the simulation maintains enough fidelity through each step, preventing small errors from accumulating and derailing the entire model.

2. Geospatial Data: Latitude and longitude coordinates, especially when dealing with high-precision mapping or GPS data, demand double-precision. A tiny error in a single-precision float could mean the difference between a building and the street next to it. For example, a latitude of `34.052235` and longitude of `-118.243683` might need to be stored and processed as double to ensure accuracy down to meters or even centimeters.

3. Statistical Analysis: Calculating standard deviations, variances, or performing regression analyses often involves squaring numbers, taking square roots, and summing many values. These operations can quickly generate numbers with long decimal tails. Double precision ensures the statistical outputs are as accurate as possible for reliable analysis.

4. Complex Financial Modeling (excluding balances): While I strongly advise against using `FLOAT` for currency balances, double-precision is often used in complex financial *models* where the calculations involve predicting market movements, option pricing, or simulating portfolio performance. These are often about probabilistic outcomes and rates of change, where high precision in intermediate steps is valued, and the final results might be approximations themselves.

5. Machine Learning Feature Engineering: In machine learning, features often involve ratios, transformations, and normalizations of raw data. These derived features can benefit from double-precision to maintain granularity, especially when the magnitude of the values varies widely.

My Take: Authority and Practical Wisdom

Having wrestled with data type issues more times than I care to admit, I’ve come to appreciate the nuance behind functions like `CDbl` and its standard SQL counterparts. It’s not just about getting data from point A to point B; it’s about preserving its integrity and meaning. I recall one project where a financial report, intended to show a client their investment growth, was consistently off by a few cents. The client, naturally, noticed. After days of digging, we traced it back to a series of implicit `REAL` (single-precision float) conversions in a complex stored procedure. Changing just a few `CAST(… AS DOUBLE PRECISION)` statements immediately resolved the discrepancy. It was a stark reminder that even the smallest rounding error can have significant real-world consequences and erode trust.

My advice, seasoned by years in the trenches, is this: Be intentional. Don’t let your database implicitly decide how to handle your numeric data if precision is a concern. Understand the data types involved in your source, your transformations, and your destination. If you’re working with data that requires high precision for calculations or represents physical measurements, `CDbl` (or `CAST AS DOUBLE/FLOAT`) is your friend. But if you’re dealing with money in the bank, stick to `DECIMAL` or `NUMERIC`. Knowing when to use which tool is a hallmark of a truly skilled data professional, and it will save you countless headaches down the line.

Frequently Asked Questions About CDbl and Double Precision in SQL

Let’s address some common questions that often arise when working with `CDbl` and double-precision numbers.

Q1: Is CDbl the same as CAST AS FLOAT?

Not exactly the “same” in terms of syntax or universal availability, but they serve the same fundamental purpose: to convert a value into a double-precision floating-point number. CDbl is a specific function found in Microsoft Access SQL and VBA environments. Its output is a 64-bit double-precision floating-point number. On the other hand, CAST AS FLOAT (or CAST AS DOUBLE PRECISION, or CONVERT(FLOAT, ...)) is the standard SQL way to achieve this in most other database systems like SQL Server, PostgreSQL, MySQL, and Oracle. In these systems, FLOAT (when used without a specific precision or as FLOAT(53) in SQL Server) typically refers to the 64-bit double-precision floating-point type, matching the precision characteristics of what CDbl provides. So, while the function name differs based on the SQL dialect, the underlying data type and the intention of achieving high-precision decimal representation are aligned.

Q2: Should I use CDbl (or equivalent) for currency?

No, you should almost never use CDbl or any floating-point data type (FLOAT, DOUBLE PRECISION, REAL) for storing or performing calculations on currency or other monetary values. The reason lies in the approximate nature of floating-point numbers. As we discussed, these types cannot always exactly represent all decimal fractions due to their binary storage format. This can lead to tiny, cumulative rounding errors over many calculations or transactions. While these errors might be minute, they can cause significant problems in financial applications where every cent must be accounted for precisely. A difference of even a fraction of a cent can invalidate financial statements, cause audit discrepancies, or lead to legal issues.

For currency, the industry-standard and highly recommended practice is to use fixed-point decimal types, such as DECIMAL or NUMERIC. These types store numbers as exact base-10 values, allowing you to specify the exact precision (total number of digits) and scale (number of digits after the decimal point). For example, DECIMAL(19, 4) can accurately store values up to 15 digits before the decimal point and precisely 4 digits after it, which is ideal for most currency applications. This guarantees that your financial calculations are always exact and free from the potential rounding issues of floating-point numbers.

Q3: How precise is DOUBLE PRECISION?

DOUBLE PRECISION, adhering to the IEEE 754 standard, offers a very high degree of precision, typically around 15 to 17 decimal digits of precision. This means it can reliably represent numbers with up to 15-17 significant digits. The total number of decimal places after the point can vary depending on the magnitude of the number. For instance, a small number like 0.00000000000000123 will retain all those leading zeros and the significant digits at the end. Conversely, a very large number like 123,456,789,012,345.678 will still have about 15-17 significant digits, but fewer digits available after the decimal point. This level of precision is generally sufficient for most scientific, engineering, and statistical computations where high accuracy is required for approximation and derived values. It offers a much greater range and more significant figures than single-precision floating-point types (which typically offer around 7 decimal digits of precision).

Q4: Can CDbl cause errors?

Yes, CDbl (and its standard SQL equivalents like CAST AS DOUBLE) can cause errors or unexpected behavior if not used carefully. The most common issues are:

1. Conversion Failure: If the `expression` you try to convert is not a valid numeric representation (e.g., trying to convert a string like “Hello World” or an empty string that cannot be interpreted as a number), CDbl will typically raise a runtime error in Access/VBA. In standard SQL, `CAST` or `CONVERT` would also throw an error or return `NULL` depending on the database system’s configuration and the specific invalid input.
2. Overflow/Underflow: While double-precision numbers have a vast range, it’s not infinite. If you try to convert a number that is larger than the maximum representable value (approx. 1.8e+308) or smaller than the minimum (approx. 4.9e-324), it will result in an overflow (resulting in Infinity) or underflow (resulting in zero or a denormalized number). While rare for most practical applications, it’s a possibility in extreme scientific calculations.
3. Precision Loss: As discussed, when converting from a `DECIMAL`/`NUMERIC` type that holds more significant digits than `DOUBLE PRECISION` can represent (e.g., `DECIMAL(38, 20)`), or when a decimal fraction doesn’t have an exact binary representation, some precision might be lost. The number will be rounded to the nearest representable double-precision value. This isn’t strictly an “error” in the sense of crashing, but it is a data alteration that could lead to logical errors in your application if not anticipated.

To mitigate these, it’s good practice to validate input data before conversion, especially for string-to-numeric conversions (e.g., using `IsNumeric()` in VBA or `TRY_CAST` in SQL Server). Additionally, understanding the source and target data types’ characteristics is key to anticipating precision-related issues.

Q5: What’s the difference between FLOAT and DOUBLE PRECISION?

The distinction between FLOAT and DOUBLE PRECISION primarily lies in the number of bits used to store the floating-point number, which directly impacts their range and precision. These terms often relate to the IEEE 754 standard for floating-point arithmetic:

1. FLOAT (often single-precision): In many contexts, especially in older SQL standards and some programming languages, `FLOAT` defaults to a single-precision floating-point number. This type uses 32 bits (4 bytes) of storage and provides approximately 7 decimal digits of precision. Its range is smaller than double-precision. In SQL Server, `FLOAT(n)` allows you to specify precision, where `FLOAT(24)` would be single-precision.
2. DOUBLE PRECISION (always double-precision): This explicitly refers to a double-precision floating-point number. It uses 64 bits (8 bytes) of storage and provides about 15-17 decimal digits of precision, along with a much wider range of values. This is the type that `CDbl` produces in Access/VBA, and it’s explicitly named `DOUBLE PRECISION` in PostgreSQL, `DOUBLE` in MySQL, and `BINARY_DOUBLE` in Oracle. In SQL Server, `FLOAT` without a specified precision often defaults to `DOUBLE PRECISION`, or you can use `FLOAT(53)` to explicitly request it.

Essentially, `DOUBLE PRECISION` is a specific, higher-precision version of a floating-point number compared to `FLOAT` when `FLOAT` is used to imply single-precision. When you encounter `FLOAT` in SQL, it’s critical to consult your specific database’s documentation, as its default behavior (whether it means single or double precision) can vary. However, when `DOUBLE PRECISION` is explicitly mentioned, it consistently refers to the 64-bit, higher-precision variant.