Database developer roadmap 2020
The transforming business model of lending software instead of selling them has increased its adoption on a much broader scale. The software lending business model has made available the large components available to software developers and they can use it to build more complex systems hence we are delivering software at a much faster pace. Process improvements have always been an integral part of organizations and that’s why the software development industry thrives as it contributes a lot in improving processes and automating tasks. The easy availability of diversified software, artificial intelligence, and lower cost to run automated digital and physical infrastructure has led the movement of industry 4.0 which surged the demand of web developers in the industry.
The gap between what’s taught in schools, colleges and what’s demanded in the software industry has led us to write this article "Database developers roadmap for 2020" in conjunction to our upcoming article “web developers roadmap for 2020”
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We have been using databases for more than three decades and they have been working pretty well. Since we have started moving towards web applications and cloud infrastructure to reduce the cost and latency, we started facing many problems with our conventional relational databases.
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Relational databases were used to store data where the aggregation point (i.e. reference point) to compute and fetch data can change a lot such as in banking transactions, although the processing of these transactions took more time they were preferred in this use case. Relational databases need to be ACID compliant to maintain the integrity of data for highly important transactions. To stay ACID compliant, we often have to lock transaction to some records that are under processing and use centralized data. The problem web developers face in the cloud era was that centralized databases were increasing latency as the end-user could be sitting thousands of miles away from the server where the application is running, this latency could only be reduced if we create distributed clusters of these databases. Using the clustered approach creates another problem i.e. violating the ACID compliance, hence the whole solution needed to be implemented in a new way.
Non-relational databases were based on the ideology that we need to store a lot of data but it might not be heavily correlated for example the images stored for social media profiles. Whenever you upload a picture these platforms do not ask too much information like a form whose fields can be later on used to find a complex correlation between people in that picture. In short, the aggregation points in this type of data are drastically less hence these databases necessarily need not to be ACID compliant. Hence non-relational databases were segregated into 4 categories on the grounds of aggregation requirements.
Here’s a list of the 4 categories and some of the prominent non-relational databases in use today.
Key-value databases work on a simple principle to store a unique identifier for submitting an entry and storing data object with respect to it. The key can be later on use to request and fetch data from this database. DynamoDB, Redis, and Riak are a couple of key-value databases that we use today.
Document databases make it easier for developers to store and query data within a database by using the same document model format that they have used in their application code; it should be noted that the aggregation point in this database revolves around documents. The flexible, semi-structured, and hierarchical nature of documents, document models, and document databases allows them to evolve with applications’ requirements. The document model is usually used in storing catalogues, user profiles, and content management systems where each document is unique and evolves over time. MongoDB, Raven DB, and CouchDB are some prominent examples of document databases that we use today.
The column family database is derived from the hybrid analogy of relational databases and key-value non-relational databases. Relational databases map the address of stored data with a tuple of the column, row, and row value whereas in column family the tuple of key-value, column family, and column value is used.
Here’s an expansion of each column family in the row:
- Row Key - Each row has a unique key identifier for that row. Column families in that row are associated with this unique key identifier.
- Column - Each column contains a name, a value, and the timestamp.
- Name - This is the name of the name/value pair.
- Value - This is the value of the name/value pair.
- Timestamp - This provides the date and time that the data was inserted. This can be used to determine the most recent version of the data.
The example of such column family databases is apache Cassandra and HBase.
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The graph databases use a graph like structure where each node is an aggregation point which is connected to other nodes with edges that represent the relationship between these nodes. Since these databases have the capability to support semantic queries they are also known as semantic databases. It must be noted that among all 4 categories of non-relational databases only Graph databases are ACID compliant. Usually, in practical scenarios, the conjunction of Graph and other non-relational databases is used to design a web application that requires ACID compliance over distributed clustered architecture. Neo4j is one such prominent example of a Graph database that we use these days.
We would like to highlight that lesser the aggregation points lesser will be the processing which will consequence into almost zero latency over distributed clustered architecture. Hence database transactions that require ACID compliance for designing these kinds of web applications have become possible to an extent.
We hope this information helps in giving a boost to the skill development of aspiring web and database developers.