Difference between 'join' and 'merge'?
Suppose one wants to make polygons A and B, and their respective attributes to be one unique polygon C. What is this GIS operation called?
Is there any difference in GIS terminology about "joining" and "merging" geometries? Or does the terminology expand to different concepts besides geometries?
As an example, I could not find the answer reading tag wikis in GIS SE.
Currently, the excerpt and wiki of merge says:
In the context of GIS, merge typically refers to either
- combining two or more geometries into a new, single geometry
- combining the records of two different tables into a single table (similar to append)
- combining multiple raster files (stacked or adjacent) into one.
A specific type or method of merging is dissolve.
And the excerpt of joins states:
The process of connecting two or more datasets based on a common attribute or spatial location.
"Connecting two or more datasets based on a common attribute" seems to be the same as the second bullet in the merge wiki.
When to use join:
- When there is a one to one relationship between two distinct tables or geometrics (based on join field or spatial relation)
- Used to append new field/columns to table (result = table has same number of rows, and more columns)
When to use merge:
- To combine one to many tables with similar database schema
- Used to combine additional rows to destination table (result = table has more rows, and potentially more columns)
The bullets you listed are correct, joins are for adding additional information such fields or columns to an existing table, and merge is for creating a new table that has the combination of rows from table A, B, C… etc.
Understand SSIS Merge and Merge Join with example step by step
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Here in this article we will learn SSIS tool i.e. Merge and Merge Join step by step with an example.
What is SSIS Merge.
SSIS merge is one of the component of SSIS, available in toolbox. SSIS merge works similar way to a SQL join it merges the two or more different sources (Sources can be of same type or different type / heterogeneous) into a single output. SSIS is all about collecting data from different sources, If you want to merge the collected data from different sources then we can use merge component. Merge component accepts only 2 sorted (compulsory) inputs. if there is more than 2 inputs then it best to use Union All transformation component. Also Merge transformation have only 1 output and does not have any Error output.
Note : Source can be anything ADO.NET Source / Excel Source or any Flat File Source.
What is SSIS Merge Join.
SSIS merge join also combines data from different sources (Source of same type or different type (heterogeneous)) into single output or merged output. It uses merge concept based on (Inner, Left and full). We can use Merge Join based on specific condition like combining data on matching keys with that Inner, Left and full. Merge Join component accepts only 2 sorted (compulsory) inputs and one output and one error output.
Unlike Merge, Merge Join combines data depending on matching keys or string name.
On the same let's see an example of Merge and Merge Join one by one in step by step way.
SSIS Merge Transformation with example
Here in this we will take two data sources 1 Flat File 2 : OLE DB Source and we will load data then we will sort data why because SSIS merge transoformation accepts sorted data then we merge both sources sorted dara and display output in a flat file destination. An example is simple and quick to understand if you are new to MSBI kindly ready our previous articles.
As shown in above image just drag and drop all above shown components.
Go to SQL Management Studio and create a simple table like we did here by creating Sales Table with SalesID, SalesPerson and Address (int, varchar, varchar) data type respectively.
If you see here we have three records of keys 1001, 1002 and 1003.
Now let's create txt file for Flat File source and add some data to it as shown in below image.
If you see in above image we have added more three records i.e. 1004, 1005, and 1006. Now we will join both data source records into single output using Merge.
Here in this step we will configure both source files one by one.
So let's configure our Flat File Source file. In order to do that just right click on Flat File Source and configure it all these steps we have repeated many times in our previous articles if you still don't no then no problem just go to article section and read article. Hope that will help a lot to undetstand it.
As you see we have configured our first source properly.
Now let's configure our OLE DB source inorder to do that select OLE DB source and right click -> edit and configure it.
Now that we have successfully configured our both sources now its time to sort then either in ascending order or descending order. So input, output of both source files to Sort component.
Once done its time to configure the Sort components one by one, So right click and configure it.
If you see in our above images we have successfully sorted both components in an ascending order.
So now we have two sorted outputs let's input them to Merge Transformation component. Now just right click on it and configure it as shown in below image.
For identification we have made output columns as OutSalesID, OutSalesPerson and OutAddress respectively.
Great we are in our final step. Now we have to configure Flat File Destination so we have one output from merge component let's input to flat file destination and configure it.
All set to run this project, Let's run this project and see the output.
As you see from above output both of the data sources are merged into single file output and that we achieved using Merge Transformation Component.
SSIS Merge Join Transformation with example
We will take up same example but will do some modification in flat file source file i.e. we will remove Address and replace Salary and we will try to merge both sources i.e OLE DB and Flat File using merge join using matching keys i.e. SalesID.
Let's create a new txt source file for Sales with columns like SrcSalesID,SrcSalesPerson,SrcSalary as shown in below image.
If you see closely here we have made some changes instead of Address we replaced it to Salary why we did it because to implement merge join by combining data by their SalesID and displaying complete output including SalesID, SalesPerson, Address and Salary.
So friends let's compare both source files.
IF you see above image both source files (Flat File Source & OLE DB Source) where SslesID and SalesPerson are common in both sources, From these both source files we need to pick up marked red fields which we want to make it to output final destination by matching their.
Configure your respective source files i.e. Flat File Source with new txt file and OLE DB Source same as above used in first example.
So configure only Flat File Source file.
Keep OLE DB Source as it is.
Drag and drop sort component and configure as it we did previously for Merge Transformation.
Since we have sorted data now let's drag and drop Merge Join Transformation component and configure it. Two output of both sorted component input it to Merge Join and then right click on it and configure it as shown in below image.
Join Type : Inner, Left Outer Join and Full Outer Join.
If you want to display only matching columns then go with Inner Join. If you want to display complete left table column values irrespective of matching with right table then go with Left Outer Join. If you want to display both table columns then Full Outer Join. Since here i was needed only matching columns so we choose Inner Join and selected only those columns which i want to display it on Destination Flat File.
Configure Flat File Destination : We came to final step just drag and drop Flat File Destination component and output of merge join make a input to it and right click and configure it.
Celebration time : All done just run this project and enjoy the output.
So friends as you saw how we have merged multiple sources into single output. Merge and Merge Join a useful component when we are working with multiple sources to convert them to single output. Hope you like this session we will come up with more topics so stay tuned with us coz we weekly post a new article. Kindly share this article on your social accounts.
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I went to Azure Active Directory > Devices > All Devices. In that when I check the join type I see three different types mentioned for different devices. So System 1 has join type as Hybrid Azure AD joined, System 2 has Azure AD joined, System 3 has Azure AD Registered. What is the difference between these 3?
You can find the details about each method in below documents:
Please do not forget to "Accept the answer" wherever the information provided helps you. This will help others in the community as well.
@sandeepnambiar-8203 Please do not forget to "Accept the answer" wherever the information provided helps you to help others in the community.
Choice depends on the who owns the data and who gets to manage the device and what type of user id is used to authenticate.
Hybrid Azure AD Joined is for:
corporate owned and managed devices
Authenticated using a corporate user id that exists at local AD & on AAD.
Authentication can be done using both: On-Prem AD & Azure AD.
Azure AD Joined is for
Corporate owned and managed devices
Authenticated using a corporate id that exists on Azure AD
Authentication is only through AAD.
AAD Registed Device is for
Personally owned corporate enabled
Authentication to the device is with a local id or personal cloud id
Authentication to corporate resources using a user id on AAD.
Types of joins
There are four basic types of joins: inner joins, outer joins, cross joins, and unequal joins. Cross joins and unequal joins are advanced join types and are rarely used, but you should know about them to have a full understanding of how joins work.
Inner joins: only related data from both tables combined
An inner join is one in which Access only includes data from a table if there is corresponding data in the related table, and vice versa. Most of the time, you will use inner joins. When you create a join and don’t specify what kind of join it is, Access assumes you want an inner join. Inner joins are useful because they let you combine data from two sources based on shared values – so you only see data when there’s a complete picture.
Outer joins: all the related data combined correctly, plus all the remaining records from one table
An outer join is like an inner join, but adds the remaining rows from one of the tables. Outer joins are directional: a left outer join includes all the records from the left table – the first table in the join – and a right outer join includes all the records from the right table – the second table in the join.
Full outer joins: all the data, combined where feasible
In some systems, an outer join can include all rows from both tables, with rows combined when they correspond. This is called a full outer join, and Access doesn’t explicitly support them. However, you can use a cross join and criteria to achieve the same effect.
Cross joins: all the data, combined every possible way
Most of the time, a cross join is a side effect of adding two tables to a query and then forgetting to join them. Access interprets this to mean that you want to see every record from one table combined with every record from the other table – every possible combination of records. Because no data can be combined, this kind of join rarely produces useful results. But there are a few cases when a cross join is just what you need.
Unequal joins: like a regular join, but using a different comparison to combine rows
Unequal joins use an operator other than the equal sign (=) to compare values and determine whether and how to combine the data. Unequal joins aren’t explicitly supported, but you can use a cross join and criteria to achieve the same effect.
Database-style DataFrame or named Series joining/merging¶
pandas has full-featured, high performance in-memory join operations idiomatically very similar to relational databases like SQL. These methods perform significantly better (in some cases well over an order of magnitude better) than other open source implementations (like base::merge.data.frame in R). The reason for this is careful algorithmic design and the internal layout of the data in DataFrame .
See the cookbook for some advanced strategies.
Users who are familiar with SQL but new to pandas might be interested in a comparison with SQL .
pandas provides a single function, merge() , as the entry point for all standard database join operations between DataFrame or named Series objects:
left : A DataFrame or named Series object.
right : Another DataFrame or named Series object.
on : Column or index level names to join on. Must be found in both the left and right DataFrame and/or Series objects. If not passed and left_index and right_index are False , the intersection of the columns in the DataFrames and/or Series will be inferred to be the join keys.
left_on : Columns or index levels from the left DataFrame or Series to use as keys. Can either be column names, index level names, or arrays with length equal to the length of the DataFrame or Series.
right_on : Columns or index levels from the right DataFrame or Series to use as keys. Can either be column names, index level names, or arrays with length equal to the length of the DataFrame or Series.
left_index : If True , use the index (row labels) from the left DataFrame or Series as its join key(s). In the case of a DataFrame or Series with a MultiIndex (hierarchical), the number of levels must match the number of join keys from the right DataFrame or Series.
right_index : Same usage as left_index for the right DataFrame or Series
how : One of 'left' , 'right' , 'outer' , 'inner' . Defaults to inner . See below for more detailed description of each method.
sort : Sort the result DataFrame by the join keys in lexicographical order. Defaults to True , setting to False will improve performance substantially in many cases.
suffixes : A tuple of string suffixes to apply to overlapping columns. Defaults to ('_x', '_y') .
copy : Always copy data (default True ) from the passed DataFrame or named Series objects, even when reindexing is not necessary. Cannot be avoided in many cases but may improve performance / memory usage. The cases where copying can be avoided are somewhat pathological but this option is provided nonetheless.
indicator : Add a column to the output DataFrame called _merge with information on the source of each row. _merge is Categorical-type and takes on a value of left_only for observations whose merge key only appears in 'left' DataFrame or Series, right_only for observations whose merge key only appears in 'right' DataFrame or Series, and both if the observation’s merge key is found in both.
validate : string, default None. If specified, checks if merge is of specified type.
“one_to_one” or “1:1”: checks if merge keys are unique in both left and right datasets.
“one_to_many” or “1:m”: checks if merge keys are unique in left dataset.
“many_to_one” or “m:1”: checks if merge keys are unique in right dataset.
“many_to_many” or “m:m”: allowed, but does not result in checks.
Support for specifying index levels as the on , left_on , and right_on parameters was added in version 0.23.0. Support for merging named Series objects was added in version 0.24.0.
The return type will be the same as left . If left is a DataFrame or named Series and right is a subclass of DataFrame , the return type will still be DataFrame .
merge is a function in the pandas namespace, and it is also available as a DataFrame instance method merge() , with the calling DataFrame being implicitly considered the left object in the join.
The related join() method, uses merge internally for the index-on-index (by default) and column(s)-on-index join. If you are joining on index only, you may wish to use DataFrame.join to save yourself some typing.
Brief primer on merge methods (relational algebra)¶
Experienced users of relational databases like SQL will be familiar with the terminology used to describe join operations between two SQL-table like structures ( DataFrame objects). There are several cases to consider which are very important to understand:
one-to-one joins: for example when joining two DataFrame objects on their indexes (which must contain unique values).
many-to-one joins: for example when joining an index (unique) to one or more columns in a different DataFrame .
many-to-many joins: joining columns on columns.
When joining columns on columns (potentially a many-to-many join), any indexes on the passed DataFrame objects will be discarded.
It is worth spending some time understanding the result of the many-to-many join case. In SQL / standard relational algebra, if a key combination appears more than once in both tables, the resulting table will have the Cartesian product of the associated data. Here is a very basic example with one unique key combination:
Here is a more complicated example with multiple join keys. Only the keys appearing in left and right are present (the intersection), since how='inner' by default.
The how argument to merge specifies how to determine which keys are to be included in the resulting table. If a key combination does not appear in either the left or right tables, the values in the joined table will be NA . Here is a summary of the how options and their SQL equivalent names:
Use keys from left frame only
Use keys from right frame only
Use union of keys from both frames
Use intersection of keys from both frames
You can merge a mult-indexed Series and a DataFrame, if the names of the MultiIndex correspond to the columns from the DataFrame. Transform the Series to a DataFrame using Series.reset_index() before merging, as shown in the following example.
Here is another example with duplicate join keys in DataFrames:
Joining / merging on duplicate keys can cause a returned frame that is the multiplication of the row dimensions, which may result in memory overflow. It is the user’ s responsibility to manage duplicate values in keys before joining large DataFrames.
Checking for duplicate keys¶
Users can use the validate argument to automatically check whether there are unexpected duplicates in their merge keys. Key uniqueness is checked before merge operations and so should protect against memory overflows. Checking key uniqueness is also a good way to ensure user data structures are as expected.
In the following example, there are duplicate values of B in the right DataFrame . As this is not a one-to-one merge – as specified in the validate argument – an exception will be raised.
If the user is aware of the duplicates in the right DataFrame but wants to ensure there are no duplicates in the left DataFrame, one can use the validate='one_to_many' argument instead, which will not raise an exception.
The merge indicator¶
merge() accepts the argument indicator . If True , a Categorical-type column called _merge will be added to the output object that takes on values:
Merge key only in 'left' frame
Merge key only in 'right' frame
Merge key in both frames
The indicator argument will also accept string arguments, in which case the indicator function will use the value of the passed string as the name for the indicator column.
Merging will preserve the dtype of the join keys.
We are able to preserve the join keys:
Of course if you have missing values that are introduced, then the resulting dtype will be upcast.
Merging will preserve category dtypes of the mergands. See also the section on categoricals .
The category dtypes must be exactly the same, meaning the same categories and the ordered attribute. Otherwise the result will coerce to the categories’ dtype.
Merging on category dtypes that are the same can be quite performant compared to object dtype merging.
Merge node is a control node that brings together multiple incoming alternate flows to accept single outgoing flow. There is no joining of tokens. Merge should not be used to synchronize concurrent flows.
For example, if a decision is used after a fork, the two flows coming out of the decision need to be merged into one before going to a join otherwise, the join will wait for both flows, only one of which will arrive.
All edges coming into and out of a merge node must be either object flows or control flows.
The notation for a merge node is a diamond-shaped symbol with two or more edges entering it and a single activity edge leaving it.
Merge node with three incoming edges and a single outgoing edge
The functionality of merge node and decision node can be combined by using the same node symbol, as illustrated below. This case maps to a model containing a merge node with all the incoming edges shown in the diagram and one outgoing edge to a decision node that has all the outgoing edges shown in the diagram.
Merge node and decision node combined using the same symbol
Categories of Joins¶
The pd.merge() function implements a number of types of joins: the one-to-one, many-to-one, and many-to-many joins. All three types of joins are accessed via an identical call to the pd.merge() interface the type of join performed depends on the form of the input data. Here we will show simple examples of the three types of merges, and discuss detailed options further below.
Perhaps the simplest type of merge expresion is the one-to-one join, which is in many ways very similar to the column-wise concatenation seen in Combining Datasets: Concat & Append. As a concrete example, consider the following two DataFrames which contain information on several employees in a company:
Comparing Your Content
DeltaXML comparison software enables you to find and present all the relevant changes within your XML documents or files.
Its sophisticated algorithms interpret the structure of your XML and work out where the real changes are. The results can be transformed into whatever form your systems or processes require.
DeltaXML’s enterprise-strength toolkit has a flexible API that can be integrated transparently into almost any system or product.
Merging Your Content
DeltaXML merge software allows you to intelligently recombine multiple documents or datasets into a single file.
It interprets the underlying XML structure to identify all of the meaningful changes and merge them into a complete, marked-up file. The results are easily viewed in an XML editor or can be processed using custom rules.
This powerful toolkit has a flexible API that can be integrated transparently into almost any system or product.
Some users of cartographic software may need attached options and databases to come up with very high profile maps in the same program. On one hand, the cartographic programs may lack some functions for modeling, storage and management of the interconnected data. On the other hand, GIS software may not be as user friendly as they need to be and may lack some friendly symbolization and editing tools. Both of these softwares have some differences and similarities that may supplement or complement each other in terms of functionality. Below are some of the differences between Cartography and GIS.
Geographic Information System (GIS):
Presentation of modeled real world elements: A GIS system is mainly used in the representation of real world elements through the use of models that resemble the real world objects.
Topology concept is important: In a GIS system the concept of topology in the representation of the real world models is essential in the modeling of the objects to come up with a replica of the real world object.
Strict use of layer technique: A GIS system includes strict usage of layer techniques to represent the models. For instance, the system may prevent customization of bridges and underpasses.
Database definition: In a GIS system, the meaning of objects is defined by their attributes that are stored in a given database.
Manipulation and analysis functions: A GIS system comprises of tools and techniques that are used in the manipulation and analysis of the data.
No generalization: A GIS system does not allow generalization of any input data. Each data input has to be specific in order to come up with accurate models of the representations.
No WYSIWYG: The presentation created in a GIS system is not necessarily “what you see is what you get”
Raster and vector layers: Integration of raster layers and switching between the different models may be possible.
Simple printing and plotting options only: Because of the complexity of the GIS system, there are very few printing and plotting tools to be employed in the data output.
Complex to use: A GIS system is extremely complex to use and requires specialized training to analyze and manipulate the data being fed into the system.
Computer Aided Cartography software:
Representations of objects by symbols: Cartographic software is mainly used in the representation of real world objects using symbols.
Graphical representations only: In a Cartographic software, the concept of topology is not essential hence only graphical representations are necessary. This means there is less data manipulation required.
No strict use of layer technique: A cartographic software does not require strict use of layering technique unless when under special circumstances e.g, in the representation of bridges and underpasses.
Symbolic definition: In cartographic software, the meaning of objects is defined strictly by their symbolization. This comes as a disadvantage because the end representation may not be out-rightly identified by a third party unless there is prior information available.
2D visualization functions: Cartographic software contains 2D visualization and configuration options to analyze and represent the data.
Generalization of input data: In a cartographic software, a lot of the input data is generalized and the represented information may not be as accurate as it should be.
WYSIWYG: In cartographic software, the data representation follows the “what you see is what you get” analogy since all end representations come in the form of symbols.
Raster and vector layers: In a cartographic software, the raster and vector layers are usually combined together to give a symbolic representation of the data.
Complex printing output: The output options in cartographic software are usually conceived for the production of high quality printing options.
Simple to use: Unlike a GIS system, the data collected by cartographic software is easy to manipulate and hence the software is relatively easy to use. The usage of CAC software is mainly pegged on Desktop Publishing Programs that are easy to use and comprehend.
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Milky Way and Andromeda galaxies are already merging
View larger. | Andromeda galaxy actual size? Yes. This image truly depicts what the night sky would look like if the Andromeda galaxy – the galaxy next door – were brighter. Original background shot of the moon by Stephen Rahn. Andromeda galaxy image via NASA. Composite photo by Tom Buckley-Houston. The composite showed up on Reddit a few years ago. Not convinced? Here’s a similar image via APOD.
The image above shows how the neighboring Andromeda galaxy occupies about the width of six moon-diameters on our sky’s dome. But, of course, the galaxy isn’t nearly this bright. You need a dark sky to see it, and, even then, it’s a barely visible fuzzy patch of light. In order to appear as bright as in the image above, the Andromeda galaxy would need to be closer. If it were close enough to look so bright, it would appear even bigger on our sky’s dome. And that’s going to happen someday! The Andromeda galaxy is currently racing toward our Milky Way at a speed of about 70 miles (110 km) per second. Ultimately, the two galaxies will collide and merge.
Normally, the news that the Milky Way and Andromeda are going to collide is not a very headline-causing piece of information, since it is due to take place in about 5 billion years. But, new research published in the peer-reviewed Astrophysical Journal in August this year reveals that the timeline has been revised: The collision has already started!
View larger. | Here’s another composite image showing the true size in our sky of the Andromeda galaxy. This one is from astrophotographers Adam Block and Tim Puckett. It was the Astronomy Picture of the Day for August 1, 2013.
That news about the Andromeda galaxy came from Project AMIGA, which uses the Hubble Space Telescope to look at the deep-space surroundings of the Andromeda galaxy. AMIGA stands for Absorption Map of Ionized Gas in Andromeda. NASA reported on this study earlier this year, calling it “the most comprehensive study of a halo surrounding a galaxy.”
The Andromeda galaxy, our Milky Way and other galaxies all sit enshrouded in a large envelope – a so-called galactic halo – which consists of gas, dust and stray stars. The halos of galaxies are faint, so faint that detecting them is not an easy feat. These astronomers measured the size of the halo of the Andromeda galaxy by looking at how light from background quasars had been absorbed by it. They were surprised to find that the Andromeda galaxy’s halo stretches much, much farther out beyond the visible boundaries of the galaxy than previously known.
In fact, it extends as far as half the distance to our Milky Way (1.3 million light-years) and even farther in other directions (up to 2 million light-years).
Does this mean the halos of the Andromeda and Milky Way galaxies are touching?
It turns out that, from our vantage point inside the Milky Way, we cannot easily measure the characteristics of our galaxy’s halo. However, because the two galaxies are so similar in size and appearance, scientists assume that the halo of the Milky Way would also be similar.
In other words, it’s the faint halos of the galaxies that indeed appear to have started to touch one another. Thus, in a manner of speaking, the collision between our two galaxies has already started.
Observing 43 background quasars, scientists used the Hubble Space Telescope to map out the halo of the closest spiral galaxy to our Milky Way, the Andromeda galaxy. The light from these very distant quasars (emission from very bright galaxies fueled by a central supermassive black hole) is absorbed as it travels through the halo, and by studying the change in absorption depending on where in the halo you look, scientists not only see the large extent of the halo but also what it consists of. Illustration via NASA/ ESA/ E. Wheatley (Space Telescope Science Institute) This illustration depicts what the Andromeda galaxy’s gaseous halo might look like if it were visible to humans on Earth. At 3 times the size of the Big Dipper, the halo would be easily the biggest feature on the nighttime sky, according to NASA. Recent measurements of the halo show that the collision between the Milky Way and Andromeda galaxies has already begun. Image via NASA/ ESA/ J. DePasquale and E. Wheatley (STScI)/ Z. Levay.
Not taking the halo in account, the bulk of the Andromeda galaxy is now about 2.5 million light-years away from us, and getting closer all the time. As stated above, as the Andromeda galaxy comes closer, it will appear larger in our sky.
Between now and the eventual merger, any beings alive on Earth will see it get bigger and bigger and BIGGER in our night sky.
The artist’s concepts below, released by NASA in 2012, show what will happen to Earth’s night sky as the Andromeda galaxy hurtles toward us.
The descriptions below are based on painstaking Hubble Space Telescope measurements of the motion of the Andromeda galaxy, followed by computer modeling of the inevitable future collision between the two galaxies. A series of studies published in 2012 showed that – rather than glancing off each other, as merging galaxies sometimes do – our Milky Way galaxy and the Andromeda galaxy will in fact merge to form a single big elliptical, or football-shaped, galaxy.
View larger. | This series of illustrations shows the predicted merger between our Milky Way galaxy and the neighboring Andromeda galaxy.
First row, left: Present day.
First row, right: In 2 billion years the disk of the approaching Andromeda galaxy is noticeably larger.
Second row, left: In 3.75 billion years Andromeda fills the field of view.
Second row, right: In 3.85 billion years the sky is ablaze with new star formation.
Third row, left: In 3.9 billion years, star formation continues.
Third row, right: In 4 billion years Andromeda is tidally stretched and the Milky Way becomes warped.
Fourth row, left: In 5.1 billion years the cores of the Milky Way and Andromeda appear as a pair of bright lobes.
Fourth row, right: In 7 billion years the merged galaxies form a huge elliptical galaxy, its bright core dominating the nighttime sky.
Image via NASA/ ESA/ Z. Levay and R. van der Marel, STScI/ T. Hallas/ A. Mellinger.
By the way, the Milky Way and Andromeda galaxies won’t be the only ones involved in this merger. As shown in the video below, the other large galaxy in our Local Group of galaxies – M33, aka the Triangulum galaxy – will also play a role.
In the video below, you’ll recognize the Triangulum galaxy as the smaller object near the Andromeda and Milky Way galaxies. Although the Triangulum galaxy likely won’t join the merger, it may at some point strike our Milky Way while engaged in a great cosmic dance with the two larger galaxies.
Across the universe, galaxies are colliding with each other. Astronomers observe galactic collisions – or their aftermaths – with the aid of powerful telescopes. In some ways, when a galactic merger takes place, the two galaxies are like ghosts they simply pass through each other. That’s because stars inside galaxies are separated by such great distances. Thus the stars themselves typically don’t collide when galaxies merge.
That said, the stars in both the Andromeda galaxy and our Milky Way will be affected by the merger. The Andromeda galaxy contains about a trillion stars. The Milky Way has about 300 billion stars. Stars from both galaxies will be thrown into new orbits around the newly merged galactic center. For example, according to scientists involved in the 2012 studies:
It is likely the sun will be flung into a new region of our galaxy …
… our Earth and solar system are in no danger of being destroyed.
How about life on Earth? Will earthly life survive the merger? Astronomers say that the luminosity, or intrinsic brightness, of our sun is due to increase steadily over the next 4 billion years. As the sun’s luminosity increases, the amount of solar radiation reaching the Earth will also increase. It’s possible that – by 4 billion years from now – the increase in the Earth’s surface temperature will have caused a runaway greenhouse effect, perhaps similar to that going on now on the planet next door, Venus, whose surface is hot enough to melt lead. No one expects to find life on Venus (at least no one used to until very recently).
Likewise, it seems likely life on Earth will not exist 4 billion years from now.
What’s more, our sun is evolving, too. It’s expected to become a red giant star eventually. The sun’s outer layers will swell into the space of the solar system so that Earth itself is swallowed by the sun’s outer layers. That’s expected to happen about 7.5 billion years from now.
Perhaps by that time, some earthly inhabitants will have become space-faring. Perhaps we’ll have left Earth, and even our solar system. Do you think so? Let us know in the comments below.
View larger. | Artist’s concept of a stage in the predicted merger between our Milky Way galaxy and the neighboring Andromeda galaxy, as it’ll unfold over the next several billion years. In this image, representing Earth’s night sky in 3.75 billion years, Andromeda (left) fills the field of view and begins to distort the Milky Way with tidal pull. Image via NASA/ ESA/ Z. Levay and R. van der Marel, STScI/ T. Hallas/ A. Mellinger.
Bottom line: Billions of years from now, our Milky Way galaxy and the Andromeda galaxy are expected to merge. New data show that the outer galaxy boundaries have likely already started colliding. This post contains photos and video illustrating the impending merger and showing how the Andromeda galaxy will appear in Earth’s night sky over the next 7 billion years.