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Time Converter, Common Units


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s — Second. Conversion Chart

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Common units

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second to calendar month
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second to anomalistic year
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second to tropic year
second to draconic year
second to synodic month
second to syderic month
second to anomalistic month
second to draconic month


anomalistic year
syderic year
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draconic year
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syderic month
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draconic month

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Natural units

In physics, natural units are physical units of measurement based only on universal physical constants. The origin of their definition comes only from properties of nature and not from any human construct.

second to Planck time


Planck time

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Caliber to MM Conversion Chart for All Cartridges – Backfire


ByJim Harmer
Updated on

Many shooters are confused because there are two different systems for measuring the bore diameter for a cartridge: (1) the caliber system measuring the diameter of the bore of the barrel in inches, and (2) the same measurement, but in millimeters. This page will explain the problem as clearly as possible.

The manufacturers of ammunition have done us no favors in making this easy to understand, but I’ve put together this handy resource so you can quickly see the equivalent naming conventions between mm and inches (caliber).


Rifle Cartridge Caliber to Metric Conversion Table

Rifle Caliber Name Common Metric Name Bullet Diameter
.17 4.5mm .172″ (4. 32mm)
.204 5.2mm .204″ (5.2mm)
.223 5.56mm .224″ (5.7mm)
.22 (lr) 5.6mm .223″ (5.66mm)
.22 (not lr) 5.6mm .224″ (5.7mm)
.243 6mm .243″ (6.17mm)
.257 6.53mm .257″ (6.53mm)
.260 6.5mm .264″ (6.71mm)
.270 6.8mm .277″ (7.04mm)
.280 7mm .284″ (7.21mm)
.30 7.62mm .308″ (7.82mm)
.325 8mm 3.23″ (8.2mm)
.33 8.6mm .338″ (8.59mm)
.366 9.3mm .366″ (9.3mm)
.375 9.5mm .375″ (9.53mm)
.416 10.6mm .416″ (10.57mm)
.450 11.4mm .452″ (Bushmaster) or .458″ (Socom)
.50 13mm . 51″ (12.95mm)

If you’re like me, looking at the table above starts to make your head spin. There are some things in that table that just don’t seem right. That’s because the naming conventions for many of the most popular cartridges bend the rules of measuring in order to come up with a nice sounding name.

For example, the good old .308 Winchester is so named because the bullet is .308″ in diameter. Converted to metric, that’s 7.82mm, but most people would refer to it as a 7.62 because of its military distinction. The difference is that one measurement is the distance between the grooves (the cut out portions of the rifling in the barrel, and another measurement uses the measurement to the lands (the raised portion of the rifling). To make matters worse, the .308 is commonly referred to as a .30 caliber cartridge, which isn’t actually correct at all.

As you can see, the naming conventions of cartridges is complicated because ammo manufacturers like using clean, even numbers and to ignore the actual measurements.

Because the table above has to make some generalizations about particular cartridges, I’ve included the table below which lists each rifle cartridge separately so you can see the specific bullet diameter in inches and its exact metric conversion.

Common Handgun Calibers Converted to MM and Inches

Handgun Cartridge Bullet Diameter in Inches Bullet Diameter in MM
.22 LR .223″ 5.66mm
.357 Magnum .357″ 9.1mm
.380 ACP .355″ 9mm
.38 Special .357″ 9.1mm
9mm .355″ 9.02mm
.40 S&W .40″ 10mm
.44 Magnum .429″ 10.9mm
.45 ACP .452″ 11.5mm
.50 AE .50″ 12.7mm

Fortunately, understanding the conversions between metric and imperial for handgun calibers is quite a bit easier because there are far fewer common pistol cartridges than there are rifle cartridges.

For the most part, handgun calibers are either 9, 10, 11, or 12mm.


List of Rifle Cartridges and Their Bullet Diameters in Inches and MM

Cartridge Caliber Bullet Frontal Area (in2) Avg Muzzle Energy (ft-lbs)
.17 Hornet 0.172 0.023 622
.17 WSM 0.172 0.023 398
.17 HMR 0.172 0.023 240
.204 Ruger 0.204 0.033 1,325
.22lr 0.223 0.039 133
.220 Swift 0.224 0.039 1,766
.224 Valkyrie 0.224 0.039 1,519
.22-250 0.224 0.039 1,654
.22 Nosler 0.224 0.039 1,613
.22 Creedmoor 0. 224 0.039 1,769
.22 WMR 0.224 0.039 276
.223 / 5.56 0.224 0.039 1,499
.22 Hornet 0.224 0.039 680
.222 Remington 0.224 0.039 1,165
.243 Winchester 0.243 0.046 1,958
6mm BR 0.243 0.046 1,712
.240 Weatherby Magnum 0.243 0.046 2,099
6mm Remington 0.243 0.046 1,953
6mm Creedmoor 0.243 0.046 2,125
.25-06 Remington 0.257 0.052 2,201
.257 Weatherby Magnum 0.257 0.052 2,675
6.5 Creedmoor 0.264 0.055 2,231
6.5 PRC 0.264 0.055 2,780
.260 Remington 0. 264 0.055 2,273
6.5-300 Weatherby Mag 0.264 0.055 3,395
.26 Nosler 0.264 0.055 3,125
6.5-284 Norma Match 0.264 0.055 2,462
6.5 x 55 Swedish Mauser 0.264 0.055 1,983
.264 Winchester Magnum 0.264 0.055 2,766
6.5 Weatherby RPM 0.264 0.055 3,098
6.5 Grendel 0.264 0.055 1,447
.270 Winchester 0.277 0.060 2,862
.270 WSM 0.277 0.060 3,072
.270 Weatherby Magnum 0.277 0.060 3,176
6.8 Western 0.277 0.060 3,011
6.8 Remington SPC 0.277 0.060 1,624
.27 Nosler 0.277 0.060 3,513
7mm-08 Remington 0. 284 0.063 2,528
7mm Rem Mag 0.284 0.063 3,122
.28 Nosler 0.284 0.063 3,678
.280 Ackley Improved 0.284 0.063 2,912
.280 Remington 0.284 0.063 2,873
7mm Weatherby Magnum 0.284 0.063 3,482
7 SAUM 0.284 0.063 3,004
7 STW 0.284 0.063 3,458
7mm Mauser 0.284 0.063 2,330
7 WSM 0.284 0.063 3,255
.308 Winchester 0.308 0.075 2,784
.300 Winchester Magnum 0.308 0.075 3,827
.30-06 Springfield 0.308 0.075 3,179
.300 WSM 0.308 0.075 3,718
. 300 Weatherby Magnum 0.308 0.075 4,092
.300 PRC 0.308 0.075 4,246
.300 RUM 0.308 0.075 4,135
.30-30 Winchester 0.308 0.075 1,942
.300 Blackout 0.308 0.075 998
7.62 x 39mm 0.308 0.075 1,608
.30 Nosler 0.308 0.075 4,111
.300 Ruger (RCM) 0.308 0.075 2,948
.30-378 Weatherby Mag 0.308 0.075 4,666
.325 WSM 0.323 0.082 3,596
.338 Win Mag 0.338 0.090 4,164
.338 Lapua Magnum 0.338 0.090 4,851
.338 Federal 0.338 0.090 3,340
.340 Weatherby Magnum 0.338 0.090 4,674
. 33 Nosler 0.338 0.090 4,799
.338-378 Weatherby Mag 0.338 0.090 5,035
.338 RUM 0.338 0.090 4,694
.350 Legend 0.357 0.100 1,907
.35 Whelen 0.358 0.101 3,932
9.3 x 62mm Mauser 0.366 0.105 4,017
.375 H&H 0.375 0.110 4,560
.375 Ruger 0.375 0.110 4,780
.378 Weatherby Magnum 0.375 0.110 6,004
.416 Ruger 0.416 0.136 5,498
.416 Remington Magnum 0.416 0.136 5,123
.416 Rigby 0.416 0.136 5,166
.444 Marlin 0.429 0.145 3,067
.450 Bushmaster 0.452 0.160 2,810
. 45-70 Govt 0.458 0.165 3,138
.458 Win Mag 0.458 0.165 5,063
.50 BMG 0.51 0.204 12,600

Jim Harmer

Jim Harmer is a host of the Backfire Youtube channel. He has managed multiple gun ranges, and has hunted around the world. He is a well-known entrepreneur, having started many successful online brands, and lives in St George, Utah.

DFD (Data Flow Diagram) Diagrams — why are they needed and what are they / Habr

Hello everyone!

Today I decided to write a basic theory about the use of data flow diagrams as one of the process modeling tools.

The diagram displays data flows between systems, databases. Key elements are input/output data, systems, data storage and collection points.

Why do we need DFD diagrams?

DFD diagrams, unlike other notations, allow you to visually show all processes in terms of data. This might be helpful:

  • when developing an information system;

  • for system integration;

  • when migrating data and functionality from one system to another;

  • in projects related to Data Management;

  • in the process of building an analytical repository, BI solution.

The diagram allows you to visualize both the movement of data between system objects and data transformations that can be applied at different steps of the process.

DFD diagram elements

There are 4 elements in the diagram:

  1. Process.

    Processes in which there is a change in the data flow (processing, transformation, and other changes). The process, as in other diagrams, is usually written using a verb, for example: “Submitting a completed form”.

  2. External entity.

    An entity (object) that receives or sends data when interacting with the described process.

  3. Data storage.

    All data stores or individual files that store source or output data, as well as all intermediate stores.

  4. Data stream.

    Data flow, which displays the direction and the data itself, which moves between external entities and data stores using processes.

Several charting rules:

  • A process must have an input and an output stream.

  • Data stores must also have input and output data streams.

  • Data from external entities must necessarily go through the process to get into the storage.

There are also 2 different notations in DFD diagrams. Therefore, it is worth paying attention to the conventions of each element, depending on the notation used. Below is a picture of comparing elements of different notations.

DFD levels Chart

Depending on the purpose of the diagram, different levels of process detail can be displayed. For example, in order to talk and present the process to business users and customers, it is important for them to understand the context and logic of the process itself, sometimes it makes no sense to immerse them in the technical aspects of implementation. On the other hand, when talking to the technical team, it is important to focus on the implementation of the solution from a technical point of view.

As in the ER diagram for data models, which includes several layers of display (conceptual, logical, physical), DFD diagrams can also be divided into similar levels:

Shows a general description of the process that is implemented in the data flow. Displays abstractly data flows associated with different external entities

  • Logical level.

Displays the data transformation logic in the system in each process, describes. You can see the input, intermediate, output data in each process that flows from the external entity to the data stores. More points to the question “What does the process of data flow and exchange from the business side involve?”

  • Physical layer.

Includes accurate display of data stores, names of data entities. The physical layer diagram should answer the question “How will the process of transfer and data flow be implemented?”

Also often in other sources you can see the division of the chart levels into 0.1, 2, 3 and so on, depending on the level of detail.

If we are talking about developing a new solution, it is important to understand “what we have now” (AS-IS) and “what we want to get” (TO-BE). In other words, we separate our current state from the desired state that we want to achieve with our solution.


Describe the current logic diagram.


Describe the desired logic diagram with new logic and business requirements. After that, from the desired logical diagram, we describe the physical one with a new technical solution.

Telegram channel about data analytics and business analysis

KNOW INTUIT | Lecture | Design and Development of the ETL Process

< Workshop 6 || Lecture 11 : 123

Abstract: This lecture discusses the general principles of organizing the process of extracting, transforming and loading data (Extract, Transform, Load — ETL) for data warehouses, classifies data source systems, and discusses some data extraction methods. The methodology for designing ETL processes using CASE tools is considered in general terms.

Keywords: ETL, ETL process elements, data flow diagram, data transformation diagram, data transformation flow control diagrams, data extraction, data transformation, data loading, performance, extraction, transformation, database, periodicity, software, metadata, quality data, control, place, data management, Data, data cleansing, area, placement, recording, MPP, project budget, ETL process planning, multidimensional model, trigger mechanism, EBCDIC, data format conversion, denormalization, table section, information, intelligent data analysis, EII, enterprise, information, data reading, CASE, model, ILM, data transformation, control flow, replication server, data input»> Purpose of the lecture

After studying the material of this lecture, you will know:

  • what is the ETL process;
  • place of the ETL process in the architecture of a business intelligence system based on data warehouses;
  • what is an implementation of an ETL process using a staging area;
  • what is the implementation of the ETL process without the use of the staging area;
  • basic elements of an ETL process ;

and learn:

  • build data flow diagrams, data transformation diagrams, data transformation flow control diagrams;
  •»> perform general planning for the implementation of the ETL process;
  • design ETL processes.

Literature : [3], [14], [33], [
32], [51].


To make a data warehouse work, it is necessary not only to ensure the interaction of many data sources — it is important to carefully plan this interaction. Therefore, the processes of extracting, transforming and loading data play an important role in the creation and operation of a data warehouse.

To ensure that the data conversion process runs smoothly, the necessary documentation and metadata must be in place. The process of extracting data affects the performance of other systems, so it should be considered in the aspect of change management and configuration of data source systems.

The abbreviation ETL (extraction, transformation, loading — extracting, transforming and loading data) refers to the composite process of transferring data from one application or automated information system to others .

The ETL process is implemented either by developing an ETL application, or by creating a set of built-in programming procedures, or by using an ETL toolkit. ETL applications extract information from source source databases, convert it to a format supported by the target database, and then load the converted data into that database.

The goal of any ETL application is to deliver data from external systems to the system users are working with in a timely manner. As a rule, ETL applications are used when transferring data from external sources to the data warehouse of business intelligence systems. Therefore, the organization of the ETL process is an integral part of the development project of almost any data warehouse.

Designing and developing an ETL process is one of the most important tasks of a data warehouse designer. For a data warehouse, the ETL process has the following properties. Firstly, the amount of data that is selected from data source systems and placed in a data warehouse is usually quite large, up to tens of GB. Secondly, the ETL process is a necessary part of running a data warehouse. The frequency of the ETL process is determined not only by the user’s need for timely data, but also by the size of the downloaded portion of data. According to experts, the ETL process can take up to 80% of the time. Thirdly, at various stages of the ETL process, data warehouse metadata is formed and data quality is ensured. Fourth, during the ETL process, data loss can occur, so it is necessary to ensure control over the flow of data into the data warehouse. Fifth, the ETL process has the property of failover without data loss.»> On
Figure 15.1 shows the place of the ETL process in the architecture of a business intelligence system based on a data warehouse.

ETL process in data warehouse architecture

As can be seen from the figure, the ETL process is entrusted with all the work of preparing data for delivery to the data warehouse, generating and updating the data warehouse metadata, and managing the data extracted as a result of Data mining.

Thus, the ETL process consists of three main stages.

  • Data extraction At this stage, data from external sources are selected and described (data warehouse metadata begins to form), which should be stored in the data warehouse (relevant data).
    Data conversion At this stage, the relevant data is converted into the data representation format in the data warehouse, the transformation rules are stored in the data warehouse metadata, the key fields of the tables of the physical structure of the data warehouse are formed, and data is cleaned.
  • Loading data At this stage, data is loaded into the data warehouse, aggregates are built.

Approaches to implementing an ETL process

There are several approaches to implementing an ETL process. A common approach is to extract data from source systems, place it in a staging area of ​​disk storage (Data Staging Area), perform data transformation and cleaning procedures in this staging area, and then load the data into the data warehouse, as shown in

Implementing an ETL process using a staging area»> Placing retrieved data in a staging area means writing data to a database or disk subsystem files. An alternative approach to implementing the ETL process is to perform the transformations in-memory of the ETL server and load them directly into the data warehouse, as shown in

Implementing an ETL process without using a staging area

Converting data in RAM is faster than if it was previously placed on disk. However, the use of this approach is limited by the size of the portion of the downloaded data. If the chunk size of the data being loaded is large enough, then a staging area must be used.

Sometimes another approach to the implementation of the ETL process is used, when data transformation is performed on the data warehouse server during data loading. The use of this approach is determined by the computing capabilities of the storage server. Typically, this approach is used for MPP servers of the data warehouse.

Depending on who retrieves data from source systems, the ETL process can be implemented in the following ways.

  1. The ETL server periodically connects to systems, data sources, polls them, extracts the results of query execution and hosts them for further processing.
  2. Data source system triggers track changes to data and place the changed data in separate tables, which are then exported to an ETL server.
  3. A specially designed application in data source systems polls them periodically and exports the data to an ETL server.
  4. Source systems database logs are used, which contain all data change transactions. Changed data is retrieved from the logs and stored on the data source system server for later import into the ETL server.

Depending on whether where the process of extracting data from source systems is performed, the implementation of the ETL process can be performed in the following ways.

  1. The ETL process runs on a dedicated ETL server, which is located between the data source systems and the data warehouse server. In this case, the ETL process does not use the computing resources of the data warehouse server and data source system servers.
  2. The ETL process runs on the data warehouse server. In this case, the data warehouse server must have sufficient disk space to perform the ETL process, the use of server resources should not greatly affect the performance of user requests to the data warehouse.
    The ETL process runs on the data source system servers for the data warehouse. In this case, changes in the data are immediately reflected in the data warehouse. This approach is used in the development of real-time storage systems.

Thus, when designing the ETL process, the data warehouse designer must, based on an analysis of the requirements for the functioning of the data warehouse, together with the IT project manager, choose a software and hardware solution for the implementation of the ETL process, namely, to determine exactly where where and how the ETL process will be executed. This decision can be strongly influenced by the budget of the project. For example, there may not be enough financial resources to implement the ETL process on a dedicated server.

Designing an ETL process

As a rule, when designing an ETL process for a data warehouse, the following sequence of actions is followed.

  • Planning an ETL process, which includes developing a diagram of data flows from source systems, defining transformations, a key generation method, and a sequence of operations for each target table.
  • Designing a process for populating dimension tables, which includes developing and verifying a process for populating static dimension tables, developing and verifying change mechanisms for each dimension table.
  • Designing a process for populating fact tables, which includes designing and verifying the process for initially populating and periodically updating fact tables, building aggregations, and developing procedures for automating the ETL process.
Planning the ETL process

The data transformation process plays a very important role in the success of a data warehouse project, so it must be well planned. The development of the plan is interactive.

First, a generalized plan is created, which reflects the list of data source systems and indicates the planned target data areas (data that will be placed in the data warehouse). The source of the target data is determined based on the formulated business requirements for the data warehouse. As a rule, data sources differ significantly: from databases and text files to SMS messages. This circumstance can significantly complicate the task of data conversion.

The assignment of such high-level source descriptions gives, on the one hand, developers an idea of ​​both the system being created and existing data sources, and, on the other hand, an understanding of the complexity associated with data transformation processes for the organization’s management.

It is best to start drawing up a generalized plan when a multivariate model of the data warehouse has been developed. Then for each table of the multidimensional schema, you can define tables — data sources.


Relationships between tables of the multidimensional schema of the data warehouse and tables — data sources can be indicated using arrows, as in the diagram on

Multidimensional diagram of the HD decision support system

enlarge image

Planning source systems for ETL process

Each arrow in the diagram should be numbered and accompanied by a comment that should serve as a reminder to developers of the need to monitor the integrity of reference data or other processing specifics of each source table defined by business rules.

At this planning stage, any discrepancies found in data definitions and coding schemes should be recorded.

The detailed planning of the ETL process depends largely on the use of the selected ETL tools. To date, quite a lot of such tools have been developed both by companies producing complex solutions in the field of data storage (IBM, Oracle, MicroSoft), and by third-party software manufacturers (Sunopsis). Therefore, the task of selecting suitable ETL tools must be resolved before detailed planning can be undertaken.

This class of software is intended for extracting, generalizing, converting, cleaning and loading data into storage. There are two approaches to writing ETL procedures: 1) they can be written by hand; 2) you can use specialized ETL tools.

Each of the approaches has a number of advantages and disadvantages, so the choice of one or another method for implementing ETL procedures is determined by the requirements for the data loading subsystem in each particular case. Let’s highlight the most important advantages of each of the ways to write ETL procedures.


  • the ability to use widely used programming paradigms, such as object-oriented programming;
  • the possibility of using many existing methods and software tools to automate the process of testing the developed data loading procedures;
  • availability of human resources;
  • the ability to build the most productive solution using all the advantages of database management systems (DBMS) involved in the project when programming;
  • the ability to build the most flexible solution.

The use of ETL tools:

  •»> simplification of the development process, and, most importantly, the process of maintaining and modifying ETL procedures;
  • speeding up the system development process, the ability to use ready-made developments supplied with ETL tools;
  • the ability to use built-in metadata management systems that allow you to synchronize metadata between the DBMS, the ETL tool, and data visualization tools;
  • possibility of automatic documentation of written procedures;
  • many ETL tools provide a means to increase the performance of the data loading subsystem, which include the ability to parallelize calculations on different nodes of the system, the use of hashing, and many others.

Attention should be paid to the choice of technology for the implementation of ETL procedures, if one of the data source systems is an ERP system. Systems of this class are the most complex, as they have a very intricate data model and often contain tens of thousands of tables. To implement the procedures for loading data from ERP systems, a specialist who is familiar with this source system should be included in the development team, since the analysis of such systems from scratch takes too long. In addition, most ETL tool providers provide connectors to many ERP systems that allow you to import ERP system metadata and work with it at a higher level.
The presence of connectors to ERP systems provides specialized ETL tools with a great advantage over manually writing data loading procedures if the ERP system acts as a data source.

Once the preliminary planning has been completed, the detailed planning begins.

Detailed data transformation plans are made for all tables involved in the transformation process.

By alexxlab

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