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Automatisierte Datenkonnektoren für die Datenintegration

Automatisierte Datenkonnektoren für die Datenintegration

April 4, 2023
April 4, 2023
Automatisierte Datenkonnektoren für die Datenintegration
Benutzerdefinierter Code zur Verbindung mit Daten-APIs gehört mit zunehmender Geschwindigkeit der Vergangenheit an.

Innovative companies must collect data from various sources and store them centrally to facilitate analysis.

However, companies of all sizes find this difficult because their data exists in many external and internal sources — multiple customer-facing applications, Facebook and Google for marketing, Salesforce for CRM, Zendesk for helpdesk and more.

In the past, data integration typically involved custom code connections built by data engineers to connect data APIs. But manually building and maintaining custom code connections is time-consuming and labor-intensive.

All of this adds up and eventually overwhelms your data engineers, creating a bottleneck for your data analysts.

There’s a better way to integrate your data — automated data connectors. 

In this article, we’ll explain what data connectors are and what they do. We’ll also cover the problems data engineers face when manually developing and managing data connectors and how buying automated connectors can be the solution.

What is a data connector?

A data connector is a software process that collects raw data from various sources and ingests it into a data pipeline for integration. It consists of the data uniform resource identifier (URI), authentication method, data type and any other component required to access each data source.

data connectors

Connectors are the first step for data integration. It collects data from applications and databases used by different teams like marketing, sales and HR, along with websites and other sources relevant to your business.

Once the connector has gathered data, other mechanisms are used to load data into a destination, like a data lake system, business intelligence tool or reporting applications where it’s then transformed for analysis.

In the past, data integration required engineers and developers to code scripts to collect data from each source manually. However, data teams have since moved away from this approach because it’s expensive, labor-intensive and slow. 

They also relied on workflow management platforms for data engineering pipelines, but these tools limited the type and number of data sources you could connect to.

Both methods could still work for old-school data pipelines that use the Extract, Transform, Load (ETL) process for data integration.

Today, data analysts and scientists need fresh data delivered rapidly to guide decisions in fast-paced markets. Stale data can hinder an organization's ability to cater to customer and market trends. This delay could lead to reduced revenues and profits.

This is why most modern organizations prefer ELT data integration solutions. These cloud-based platforms are easy to set up, provide rapid data access and significantly reduce time spent on pipeline development and maintenance.

Automated pre-built data connectors, provided by solutions like Fivetran, are ideal for this since they allow fast integration, require less or no monitoring and maintenance and can be customized to match various business and analytical use cases.

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Traditional data integration approaches

Traditionally, data engineers patched together scripts and task managers. This approach was eventually superseded by tools like SSIS (SQL server integration services) and Airflow.

Each of these approaches creates different problems.

Patched-together scripts are highly labor-intensive in terms of the maintenance and management of various jobs. This problem grows considerably with scale, especially when your needs grow from a handful to dozens of data sources. 

Tools like Airflow and SSIS mostly connect to specific data sources, usually databases. These tools are used in combination with more traditional ETL-based data pipelines.

The problem with these ETL methods

Traditional methods of getting data from point A to point B with modern applications require data engineers to create custom connectors for APIs. Tools like Salesforce and Asana have APIs that make it (not that) easy to pull data.

As a data engineer, I’ve had to create connectors for these systems over and over, each time writing a new package just to deal with another REST or SOAP set of endpoints. 

Sure, with clever engineering, you can eventually turn a collection of source-specific connectors into a general solution. But custom connectors are just the beginning of your problems.

In addition to developing your connectors, data engineers also need to create systems that manage logging, dependency management and version control, as well as instill some form of CI/CD (continuous integration/continuous development). 

Once you build these initial pipelines, you’ll spend a significant amount of time maintaining them, modifying them when connectors experience issues and updating them as teams need new columns and tables built.

This is why data engineers are often the main bottleneck in the data lifecycle.

The data engineering bottleneck

All of the work associated with building connectors bogs down data engineers and frequently leads to bottlenecks. 

Data scientists and data analysts across many companies say the same thing: Their data engineers just can't keep up with their demands. Data engineers concur.

Even with robust, sophisticated software libraries like Airflow and Luigi, there are always more and more data sources to pull and integrate. This constant need to develop and maintain current pipelines and infrastructure means data engineers are constantly bogged down.

This is why most organizations are turning to automated data connectors and ELT to drive their data pipelines.

Why ELT is the future of data integration

The problem with ETL is that it’s slow and tightly coupled with the data engineering process.

ETL requires data engineers to spend a lot of time developing complex business logic before loading the data into a company's data warehouse or data lake.

To continue reducing the data engineering bottleneck, automated connector platforms use Extract, Load, Transform (ELT) instead of ETL.

elt process

The main difference between ETL and ELT is the order of operations. Instead of performing 

complex business logic before loading your data into your data storage solution, you load your data into your data warehouse or data lake. Then analysts and data engineers can apply the logic afterward.

ELT allows your analysts faster data access because of a simpler workflow and shorter project turnaround times. 

In addition, many automated connectors easily integrate with data transformation tools like dbt, allowing your team to take advantage of software development best practices like version control.

So, using automated connectors on a platform like Fivetran helps build an efficient automated ELT data pipeline without worrying about scaling, flexibility or maintenance.

The benefits of automated data connectors

Organizations can streamline their data integration and analysis workflow with automated data connectors.

benefits of automated data connectors

These connectors offer four key benefits.

Collect the most relevant data

Big data is not helpful to companies by itself. To become data-driven, analysts and data scientists need fast and timely access to their data. Traditional methods like ETL are slow — and slow data, in many cases, is just as bad as incorrect data. Companies strive to make decisions based on what’s currently happening, not what happened yesterday or last month.

Integrate with business intelligence tools

Automated data connectors allow your team to integrate with transformation tools like dbt. You can connect your automated connectors to a particular source, install the dbt packages for it and, within a day, have analytics-ready tables and aggregations that enable you to understand the performance of your support team better.

Improve productivity

Automated data connectors easily connect to various data sources with minimal configuration, coding and user input. This means your team doesn't have to develop code or infrastructure to manage a multitude of complex API connectors. 

Low-code or no-code data pipelines like this saves development and maintenance time.

If an API changes, your team will not need to encode those changes into their connectors because the vendor is responsible for them. This eliminates the need to repeatedly code the same Asana or Salesforce connectors, which many data engineers currently have to do.

Enable informed decision-making

Automated data connectors from Fivetran help your data team create a unified dashboard and centralized storage system for all their data needs. When analysts can access and aggregate data from SaaS applications and databases in a few clicks, they can gain better insights at a faster pace.

Moreover, they have comprehensive data sets at their fingertips that can be sorted and organized as needed. This helps them see all the context for a particular metric or data set and come to the correct conclusions.

So, business decisions are led by accurate, data-driven analysis rather than by insights based on a section of data.

Buy vs. build: Which method to choose

Companies still rely on multiple solutions for different departments and manual data pipeline management to gain an accurate overview of their business operations. This can slow down business intelligence efforts or even completely derail them.

To help organizations avoid this, we've compared buying vs. building data pipelines to highlight why pre-built connectors are better.

build vs buy data connectors

Let’s take a closer look.

Free your data engineers from connectors

Manually building a data connector for each source is highly labor- and time-intensive, diverting scarce engineering resources from higher-value work.

Off-the-shelf data integration tools like Fivetran have automated data connectors for common data sources that can radically reduce the time it takes to go from raw data to final data products like dashboards, with a minimum of engineering time.

This moves the pressure of developing complex data infrastructure away from data engineers. Automated connectors can help your data engineers and software engineers focus more on solving big, high-value problems instead of writing commodity code for data operations. 

Ultimately, buying automated data connectors allows your data engineers to work on more critical projects.

Save money

One of the biggest expenses of building data pipelines is setting up a team of engineers and then paying them to painstakingly plan, design and construct every element manually. This adds up quickly, with companies spending around $520,000 yearly on data engineers for pipeline construction and maintenance.

By contrast, buying connectors and other data pipeline components from a provider like Fivetran means you invest in fewer human resources, like engineers and developers. Their work hours are also reduced or spent on other more relevant tasks.

Prevent data bottlenecks

Building data connectors could work well for an individual or a small company that works with very few data sources. However, it’s not a feasible option for growing businesses or enterprises that rapidly scale the number of sources and the volume of data.

When developers and data teams are faced with these increasing demands to constantly add new connectors, modify existing ones or fully rebuild them, it creates a bottleneck.

Buying connectors, on the other hand, speeds up this process. Need to add a new source? Just use a pre-built connector to instantly integrate it into your pipeline or use a custom connector 

that handles the loading and transformation for you.

custom data connectors

When you buy automated connectors, you can scale easily to accommodate all your data needs. Analysts get access to the latest data without overloading engineers.

Access additional capabilities

When every change to your data connectors does not require hundreds of lines of code, your engineers can focus on optimizing your connectors to be more efficient.

Buying automated connectors from a platform like Fivetran gives you access to many additional features, including robust security and compliance, pre-built transformations and parallel loading for faster data processing.

Conclusion 

Automated data connectors used in fully managed ELT pipelines are essential to the modern data stack that aims to provide comprehensive data and accurate analysis quickly.

These connectors and the platforms that facilitate them, like Fivetran, eliminate most of the problems of manual data pipeline construction, save money, offer rapid access to data and simplify data pipeline management.

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