(Extract, Transform, Load), a relatively newer paradigm, operationalizes enterprise data to accelerate digital transformation. Lately, has become an essential part of data management practices, enabling enterprise to reverse the traditional ETL and process.
As the name suggests, treats the traditional ETL sources (third-party such as , database systems, and external files) as targets and traditional ETL destinations (typically a such as or Google ) as sources. When enterprise data is available at the disposal of your non- , they can use it effectively to make business decisions.
There are a few factors that determine the effectiveness of a modern. In this article, we’ll discuss five best practices that can unlock the full potential of your . In the end, we’ll discuss some questions to clarify the role of in . Let’s begin.
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Table of Contents
- Quality Data Connectors and Automated Syncing
- Data Security & Regulatory Compliance in Data Engineering Pipelines
- Fault-Tolerant Practices
- Data and Auditing
- What’s the Point of Having ETL if We Are Going to Move Data Out of the Again?
- Is the Same as ?
- Leverage No-Code with Integrate.io to Activate
1. Quality Dataand Automated Syncing
An organization uses multiple dozen tools daily. Statista reports that in 2021 organizations were using 110 on average, compared to 80 in the previous year. This is where the role of is critical. It combines data from different sources and makes it readily available for your .
must provide high-quality data or plugins that can facilitate quick data transfer. When implementing a , the organizations should consider the following factors:
Is thecompatible with their current business tools like , , Marketo, MailChimp, etc?
Can they connect with the required sources and destinations using?
Areeasy to integrate and manage?
Does theprovide transfer for large volumes of data?
Additionally, datacan automatically sync data from source warehouses to destination tools, giving them a 360-degree view of the enterprise data. For instance, your receives updates on the latest ad campaign that includes engagement, activity, and click-through rates. They can analyze this data to update their marketing quickly.
With automated, save tons of time, giving more flexibility to your and maximizing profits.
2. Data Security & Regulatory Compliance in
Securing data according to regulatory compliance is one of the most criticalbest practices. Whenever data is involved, all , including customers and regulators, are concerned with its security and privacy.
Because they deal with data,are an asset that must be secured. Multiple data touchpoints, dozens of third-party integrations, and continuous data flow from source to destination make vulnerable to security breaches. However, robust employ effective measures to ensure data safety at the source, in transit, and at the destination.
In particular,should follow two data security best practices which are as follows:
Regulatory standards: All data coming in and going out of the system must follow the regulatory standards set by GDPR, CCPA, PCI DSS, HIPAA, and SOC 2 protocols.vendors must comply with these protocols to protect personally identifiable information (PII) and enterprise data.
Data encryption:must employ robust data encryption techniques to secure the data transfer from the to third-party business tools is secure. Additionally, should secure all data backups and snapshots.
who are maintaining are also responsible for security governance. They can identify unauthorized data access to , databases, and codes by monitoring access logs and audit trails. They can protect sensitive data by running various security tests on the whole system.
Organizations cannot afford data loss due to any kind of malfunction. Imagine if enterprise data is erased or corrupted. The company would lose millions in revenue because it won’t have relevant data to communicate with its customers or perform its daily data-driven activities.
Ensuring fault tolerance is one of the most criticalbest practices. When your deal with millions or potentially billions of rows of data, errors are inevitable. However, mitigating these errors as effectively as possible to minimize damage is vital for the survival of an enterprise.
Detection and recovery are two major components of fault-tolerant systems. Modernare equipped with advanced fault-tolerant systems that can proactively catch and correct errors or recover system state in case of network failure or overload.
In fault detection, thecan use heartbeat detection to periodically check the status of the source and destination. For instance, if the destination tool does not receive a heartbeat signal from the source , then sync might have a problem. Moreover, intelligent can predict faults by analyzing the historical execution of .
A fault-tolerantis dependable, reliable, and available. It offers enough redundancy to keep the operations running. If your does not offer a robust fault-tolerant architecture, be ready to face some legal and financial troubles.
4. Dataand Auditing
is a complex process that requires careful of the entire system. In the data ecosystem, data refers to the process of tracking your data health using , notifications, and logs. Data is a critical player in the that aims to achieve minimal disruption of the process.
should have the capabilities to track five data principles which include:
Freshness: Checks if the data going into the third-party business tools is up-to-date.
Distribution: Checks if data is formatted correctly and the data values are within accepted ranges.
Volume: Checks if complete data is present and transferred (in terms of size)
: Tracks any changes to the data structure and format
Lineage: Keeps a historical track record of how the available data was generated up until it is consumed at the destination
Dataensures better governance, integrity, and reliability of the data system. should either provide features or integrate with external data tools to enable audit logs and system rollback capabilities.
Dataempowers auditing. It enables to keep track of any changes in the data flow. powered by data and auditing features offer greater transparency in their operations, giving more confidence to the during .
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Enterprise data volume is snowballing. In 2022, enterprise data volume is expected to reach 2.02 petabytes, compared to 1 petabyte in 2020. While implementing best practices, it is important to note whether the tool can scale as per business requirements.
is needed for two reasons: handling large data inflow and processing it at a greater speed. should be able to automatically scale vertically and horizontally, as per requirement.
should scale quickly without interrupting daily activities. depends on the availability of relevant data . If a data is unavailable, the should be flexible enough to support custom integrations that are easy to implement, enabling to build and manage their own plugins.
Moreover,also depends on how the data is transferred, either as a stream or in the form of batches. Both stream or batch should be able to manage massive inflow while maintaining the same processing and syncing speed.
What’s the Point of Having ETLif We Are Going to Move Data Out of the Again?
Moderndon’t work well with data. When data is gathered in centralized storage like a warehouse, it hinders many , such as evaluating KPIs and , building , or enabling across the organization.
If data is only accessible to yourteams, they will only use it to build using , which offers limited . take this data out of the and offer it to your which are running day-to-day operations and interacting with across various departments of the organization.
offers many for operational teams. For instance, the sales team would like to monitor a key customer or KPI that is usually evaluated in the . can bring this calculated to a tool like .
Isthe Same as ?
Just likeshould not be confused with ETL, it should not be confounded with as well. refers to Extract, Load, and Transform. It extracts data from disparate sources and loads it directly into the without performing the process.
offers an alternate and more effective integration compared to traditional ETL. The process is performed on an as-need basis within the , reducing the time between data extraction and delivery.
is often used with for storing the bulk of raw data. offer more flexibility and if needs to be updated for future use. It enables to transform and raw data in as per business requirements. They can use a built-in or external data build tool ( ) to transform this raw data. tools are enabled, which breaks down the complexity of the process, including aggregation, normalization, and sorting.
Once the data is gathered in the, can operationalize it. It transfers data from the (or ) into , , and tools.
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democratize data across the organization and strengthen the . It enables to focus on improving rather than building custom and maintaining to support different third-party tools.
With built-in, automate data and transfer data in , allowing different operational teams (like customer support, sales, and marketing) to leverage and make data-driven business decisions.
Integrate.io offers a user-friendly that enables even non-engineers to configure and manage robust platform using 200+ built-in and destinations. It implements best practices to deliver a robust for . Moreover, this platform provides an intuitive drag-and-drop interface with a no-code experience to set up ETL within a few clicks.
Contact us today to enable intelligent with our scalable .