Data Connectors Explained: Your ETL Data Integration Guide

What is a data connector?

Data connectors are computer programs that periodically or automatically extract data from one or more upstream databases and place it in another database. Most business intelligence, analytics, and data science applications and frameworks use data connectors.

Where is data typically stored?

The majority of firms gather enormous amounts of data sets as commercial transactions progressively move online. Traditional databases, file systems, key-value stores, object stores, and streaming data sources are just a few of the places where this data is kept.

What are some data connector capabilities?

Data connections usually include capabilities like:

Parallel loading
Encryption during data ingest
Webhook callbacks
Data materialization
Accessing data sources via proxies across firewalls

Data connectors would be less useful in practical applications without these features.

Data Connector Use Cases

Broadly, the high level use cases for data connectors are:

Real-time data visualization.
Data automation.
Data management.

Data connectors allow companies to get a full picture of their data

Insights need to be based on as much relevant data as possible to gain a 360-degree view of an area functioning and use all the data available to make that determination. This will provide you with the most complete picture of the performance of a business.
Businesses can obtain a thorough understanding of a given performance area from a range of perspectives by combining data sets from the numerous databases, files, and software that relate to that area, such as customer engagement and satisfaction.
Data connectors make it possible to seamlessly combine data from several sources into one location, usually a data warehouse, so that data analysis can be done while keeping the big picture in mind.

Data connectors can be integrated with additional BI tools

Data connectors can be combined with BI tools to further increase the utility of this data in addition to gathering it all into one place for analysts to review and get insight from.
Data connections can interact with reporting software and dashboards to produce representations that make the gathered data simple to take in and comprehend. This is especially helpful for seeing long-term patterns and trends that can be vital to the company's operations.

Data connectors boost productivity

It will take a lot of time and labor to manually gather data from many sources for analysis if a corporation decides against using a data connector.
This is because each data source must be accessed individually and transmitted from each source individually. In addition to being significantly less efficient than using a data connector, this method is also more prone to human error.
For instance, someone can forget to include data sources or fail to assemble all the pertinent data from each source.
The time-consuming nature of this operation makes it also likely that the data will be updated less regularly; otherwise, it would take up the workforce's entire schedule, making insights less current and useful.
While a data connection gathers the data, the workforce might spend their time working on higher-value jobs rather than laboriously collecting data for analysis.

Data connectors enable businesses to generate predictions supported by data

Decisions made by businesses should no longer rely solely on the opinion of individuals. Today, the majority of significant organizations base their judgments on knowledge obtained through data analysis.
It is possible to analyze historical patterns and trends and predict what may happen in the future by effectively gathering data that has been recorded over a lengthy period.
To optimize success during these periods and to correct any company operations that can be the cause of a fall in performance, this can help businesses get ready for events that are likely to have an impact on the performance of a certain area -- or all areas -- of the business.

Common data sources for data integration

SaaS apps and databases
File systems
Events
Power Platform
Microsoft Azure
Online Services

Today, there are a plethora of new data sources that are acquiring data for enterprises.

Connecting to diverse data sources, there are numerous different types of data integrators that can be employed.

Some specific examples are:

CRM systems
LinkedIn
Salesforce
GitHub
Jira
Google spreadsheets
Google Analytics
Google Ads
Shopify
BigCommerce

Typical varieties of data connectors include:

Connectors for relational databases: These connectors enable connections to relational databases like MySQL, Oracle, and SQL Server.
Connectors for flat files: These connectors let you connect to flat files like Excel, TXT, and CSV.
Big data connectors: These connectors provide connections to Hadoop, Spark, and NoSQL databases, among other big data platforms.
Cloud connectors: You can connect to cloud-based data sources like Amazon S3, Google Cloud Storage, and Microsoft Azure Storage using these connectors.
Application connectors: You may connect to numerous apps, like Salesforce, Marketo, and Google Analytics, using these connectors.
Connectors for social media: These connectors let you connect to websites like Facebook, Twitter, and LinkedIn.
Web connectors: You can connect to web APIs and web-based data sources using these connectors.
Custom connectors: These connectors may not be included in general-purpose data integration solutions since they were created particularly for a given data source.

You can access data from Google Sheets, Google Ads, Google Analytics, and other Google Marketing Platform products, among others, using free connectors created by Google.

You can connect to data from a variety of sources using Power BI Desktop. All of your social media data from Facebook, Instagram, LinkedIn, Twitter, Youtube, and many more may be integrated using several data connectors, such as DataPine.

Connect to any database type, including MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, Microsoft Azure, GCP/Google Cloud, AWS/Amazon Web Services, and several others. To connect, simply expose your database as an API and use the same methods as any other API.

Common data destinations

Data lakes like S3 + Athena
Databricks Delta
Amazon Redshift
Snowflake
PostgreSQL
Google BigQuery
Azure Synapse
Periscope
AWS Data lake
AWS S3
MySQL Server

The above list are some of the most well-liked destinations for Data Connectors.

Because they offer storage and computing that can scale fast at extremely low costs, data warehouses and data lakes have gained popularity.

The goal of analytics teams is to provide data in settings where a lot of data may be stored, searched, and altered using SQL.

Data processing: How it works

Data connectors can handle data in a variety of ways:

ETL is a standard data processing technique that involves extracting data from a source, transforming it to meet the needs of the destination, and then loading the changed data into the destination.
ELT stands for extract, load, and transform; unlike ETL, it performs the transformation stage after the data has been loaded into the destination. The destination system's ability to manage the transformation process enables greater flexibility and scalability.
Data replication entails the real-time or almost real-time copying of data from a source to a destination. This is frequently used for disaster recovery and data backup.
Data synchronization includes updating two or more data sources with changes made to the others to keep them in sync. This is frequently done to make sure that the data is the same across all systems.
Data integration is the process of merging information from various sources into a single, coherent view. To create a full view of the data for analysis or reporting reasons, this is frequently utilized.
Moving data from one system to another is known as data migration. This process usually takes place in conjunction with system upgrades or consolidation efforts.
To transfer and process data between sources and destinations, data connectors can employ one or a combination of these data processing techniques.
Using API endpoints is the most effective method for loading data from the source. To use the data for future work, we must get (extract) it from the data source.

Data extraction is possible in two different ways:

Snapshot mode: All of the data is fetched at once in this mode. snapshot fetches often involve less complex code and may be selected when only a tiny quantity of data needs to be fetched. Additionally, using snapshot fetches to propagate any records that may have been deleted at the source.

Incremental mode: In this mode of operation, only the updated data will be fetched, rather than the entire set of data. Less information needs to be fetched from the source and processed by the data connector when using incremental mode. Reduced workload on the data source, lower network/storage/computing expenses, and quicker data extraction are the results of this.

Common difficulties with data extraction

Schema Change Capture
Historical Syncs
Capturing Deletes
Error Handling

The above examples are a few of the common problems that happen frequently. It's critical to comprehend the modes that each source supports to construct the connector appropriately.

Example of data extraction in practice:

Customers using CRM Sync in their Sales Navigator implementation have the option to take advantage of current LinkedIn profile matches by using the Profile Associations API. To enhance the identities of contacts and leads in the external application, you can request and use public profile images and Sales Navigator profile links.

LinkedIn profiles can be retrieved incrementally after being retrieved in snapshot mode first.

The URL GET /custom_fields/{custom_field_gid}

To obtain a detailed definition of a custom field's metadata, you can use Asana's API.

Some fields are pertinent to a certain type because custom fields can be defined for one of several types, and these types have different data and behavior. The metadata for a custom field can first be accessed in snapshot mode.

There are two solutions open to you once you've decided to employ a data connector to collect your data and profit analytically for your company.

The first alternative is to design your data connection internally by utilizing developers you currently have or hiring one; this is a fantastic choice for firms that place a high value on security and need a data connector with a distinctive source code.

Utilizing a pre-built data connector created and managed by a platform outside of your company is the second alternative, which is typically a superior solution for most firms.

Pre-built data connectors are helpful in a variety of ways due to their nature.

FAQs About Data Connectors

How do you build a data connector?

Building a data connector involves the following steps:

Determine the data source(s) the connector will use. Identifying the data source's kind (such as database, web service, or file system), data format or protocol, and any authentication or authorization needs may be necessary.
Find out what is needed for the system or software that will use the connector. There may be requirements for performance or scalability as well as the type of data that will be accessed and the format in which it will be needed.
To develop the connector, choose a programming language and any required libraries or frameworks. The data source(s) being accessed, the needs of the program or system using the connection, and the resources available for developing and maintaining the connector will all influence the language and tools that are used.
To connect to the data source(s) and get the needed data, write some code. Depending on the program or system using the connection, this may entail creating the logic to handle authentication and authorization, parsing the data from the source, and formatting the data as necessary.
Check the connector's functionality and compliance with the specifications by testing it. This may entail making sure the connector can successfully connect to the necessary data source(s) and retrieve the needed information, as well as ensuring the information is being given in the desired format.
Install the connector in the proper setting and then customize it as necessary. This can entail setting up the connector on a server or incorporating it into an already running program.
To make sure the connector keeps working correctly and meets the requirements of the software or system using it, monitor and maintain it throughout time. To accommodate new features or data sources, this may entail updating the connector as necessary and addressing any difficulties that may develop.

What is an ETL data connector?

Data from several data sources are combined into a single, consistent data store using the data integration technique known as ETL, or extract, transform, and load. This consistent data store is then put into a data warehouse or other destination system.
ETL connectors are parts of an ETL or ELT tool that provide links to data sources (including databases and apps), constructing data pipelines and facilitating the magic of extraction and loading. There are open-source and cloud-based ETL solutions available that support anywhere between a dozen and hundreds of data interfaces.

What is a connector in data integration?

Businesses may interact directly with the APIs of the apps they want to use thanks to integration connectors. You always receive a list of third-party apps that you can link your current software solution with when you use a SaaS platform, ERP, CRM, or CMS.
The connectors are located in between the two APIs, or you might say that the APIs are the connectors' ends. Regardless of whether there was any direct form of integration between the two apps/solutions, the connectors take data from one app/solution and process it such that it is comprehensible and accessible in the other app/solution.

How do you use a data connector?

To use the data for future work, we must get (extract) it from the data source:

Snapshot mode: All of the data is fetched at once in this mode.
Incremental mode: In this mode of operation, only the updated data will be fetched, rather than the entire set of data.

Therefore, the following step is to write the data into the destination after we have fetched it (data warehouse).

Replace mode: In this mode, every successful run results in the complete replacement of the old data with the new data.
Append mode: In this mode, each successful run results in the addition of fresh data to the old data.
Merge mode: If there are no existing data, new data is inserted in this mode of operation (upserted).

What are the best data connector tools?

Here are a few examples of data connection tools that are regularly used:

1. ODBC (Open Database Connectivity) is a widely used data connection standard that enables programs to access data from a wide range of databases through a single interface. ODBC drivers are available for many common database management systems, including MySQL, Oracle, and Microsoft SQL Server.

2. JDBC (Java Database Connectivity) is a data connection API that allows Java programs to retrieve information from various databases. JDBC, like ODBC, provides a standard interface via which Java programs can communicate with various database management systems.

3. ADO.NET (ActiveX Data Objects for.NET) is a data connector API that allows .NET programs to retrieve data from many sources such as databases, web services, and XML documents.

4. ETL (extract, transform, load) tools are data connectors that extract data from many sources, transform it as needed, and load it into a target system or application. Talend, Informatica, and Pentaho are examples of ETL tools.

5. Other data integration solutions Users can connect to, extract data from, and integrate data from a variety of sources using data integration platforms, which are sophisticated data connector tools. MuleSoft, Talend, DCKAP, and Dell Boomi are a few examples of data integration solutions.