Building a data pipeline can be daunting due to the complexities involved in safely and efficiently transferring data. Companies create tons of disparate data throughout their organizations through applications, databases, files and streaming sources. Moving the data from one data source to another is a complex and tedious process.
Ingesting different types of data into a common platform requires extensive skill and knowledge of both the inherent data type of use and sources.
Due to these complexities, this process can be faulty, leading to inefficiencies like bottlenecks, or the loss or duplication of data. As a result, data analytics becomes less accurate and less useful and in many instances, provide inconclusive or just plain inaccurate results.
For example, a company might be looking to pull raw data from a database or CRM system and move it to a data lake or data warehouse for predictive analytics. To ensure this process is done efficiently, a comprehensive data strategy needs to be deployed necessitating the creation of a data pipeline.
What is a Data Pipeline?
A data pipeline is a set of actions organized into processing steps that integrates raw data from multiple sources to one destination for storage, business intelligence (BI), data analysis, and visualization.
There are three key elements to a data pipeline: source, processing, and destination. The source is the starting point for a data pipeline. Data sources may include relational databases and data from SaaS applications. There are two different methods for processing or ingesting models: batch processing and stream processing.
Batch processing: Occurs when the source data is collected periodically and sent to the destination system. Batch processing enables the complex analysis of large datasets. As patch processing occurs periodically, the insights gained from this type of processing are from information and activities that occurred in the past.
Stream processing: Occurs in real-time, sourcing, manipulating, and loading the data as soon as it’s created. Stream processing may be more appropriate when timeliness is important because it takes less time than batch processing. Additionally, stream processing comes with lower cost and lower maintenance.
The destination is where the data is stored, such as an on-premises or cloud-based location like a data warehouse, a data lake, a data mart, or a certain application. The destination may also be referred to as a “sink”.
Data Pipeline vs. ETL Pipeline
One popular subset of a data pipeline is an ETL pipeline, which stands for extract, transform, and load. While popular, the term is not interchangeable with the umbrella term of “data pipeline”. An ETL pipeline is a series of processes that extract data from a source, transform it, and load it into a destination. The source might be business systems or marketing tools with a data warehouse as a destination.
There are a few key differentiators between an ETL pipeline and a data pipeline. First, ETL pipelines always involve data transformation and are processed in batches, while data pipelines ingest in real-time and do not always involve data transformation. Additionally, an ETL Pipeline ends with loading the data into its destination, while a data pipeline doesn’t always end with the loading. Instead, the loading can instead activate new processes by triggering webhooks in other systems.
Uses for Data Pipelines:
To move, process, and store data
To perform predictive analytics
To enable real-time reporting and metric updates
Uses for ETL Pipelines:
To centralize your company’s data
To move and transform data internally between different data stores
To Enrich your CRM system with additional data
9 Popular Data Pipeline Tools
Although a data pipeline helps organize the flow of your data to a destination, managing the operations of your data pipeline can be overwhelming. For efficient operations, there are a variety of useful tools that serve different pipeline needs. Some of the best and most popular tools include:
AWS Data Pipeline: Easily automates the movement and transformation of data. The platform helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available.
Azure Data Factory: A data integration service that allows you to visually integrate your data sources with more than 90 built-in, maintenance-free connectors.
Etleap: A Redshift data pipeline tool that’s analyst-friendly and maintenance-free. Etleap makes it easy for business to move data from disparate sources to a Redshift data warehouse.
Fivetran: A platform that emphasizes the ability to unlock faster time to insight, rather than having to focus on ETL using robust solutions with standardized schemas and automated pipelines.
Google Cloud Dataflow: A unified stream and batch data processing platform that simplifies operations and management and reduces the total cost of ownership.
Keboola: Keboola is a platform is a SaaS platform that starts for free and covers the entire pipeline operation cycle.
Segment: A customer data platform used by businesses to collect, clean, and control customer data to help them understand the customer journey and personalize customer interactions.
Stitch: Stitch is a cloud-first platform rapidly moves data to the analysts of your business within minutes so that it can be used according to your requirements. Instead of focusing on your pipeline, Stitch helps reveal valuable insights.
Xplenty: A cloud-based platform for ETL that is beginner-friendly, simplifying the ETL process to prepare data for analytics.
Datacenter migration is ideal for businesses who are looking to exit or reduce on-premises datacenters, migrate workloads as is, modernize apps, or leave another cloud. Executing migrations, however, is no small task, and as a result, there are many enterprise workloads that still run in on-premises datacenters. Often technology leaders want to migrate more of their workloads and infrastructure to a private or public cloud, but they are turned off by the seemingly complex processes and strategies involved in cloud migration or lack the internal cloud skills necessary to make the transition.
Though datacenter migration can be a daunting business initiative, the benefits of moving to the cloud are well worth the effort, and the challenges of the migration process can be mitigated by creating a strategy, using the correct tools, and utilizing professional services. Datacenter migration provides a great opportunity to revise, rethink, and improve an organization’s IT architecture. It also ultimately impacts business-critical drivers such as reducing capital expenditure, decreasing ongoing cost, improving scalability and elasticity, improving time-to-market, enacting digital transformation, and attaining improvements in security and compliance.
What are Common Datacenter Migration Challenges?
To ensure a seamless and successful migration to the cloud, businesses should be aware of the potential complexities and risks associated with a datacenter migration. The complexities and risks are addressable, and if addressed properly, organizations can create not only an optimal environment for their migration project, but provide the launch point for business transformation.
Not Understanding Workloads
While cloud platforms are touted as flexible, it is a service-oriented resource and should be treated as such. To be successful in cloud deployment, organizations need to be aware of performance, compatibility, performance requirements (including hardware, software, and IOPS), required software, and adaptability to changes in their workloads. Teams need to run their cloud workloads on the cloud service that is best aligned with the needs of the application and the business.
Not Understanding Licensing
Cloud marketplaces allow businesses to easily “rent” software at an hourly rate. Though the ease of this purchase is enticing, it’s important to remember that it’s not the only option out there. Not all large vendors offer licensing mobility for all applications outside the operating system. In fact, companies should leverage existing relationships with licensing brokers. Just because a business is migrating to the cloud doesn’t mean that a business should abandon existing licensing channels. Organizations should familiarize themselves with their choices for licensing to better maximize ROI.
Not Looking for Opportunities to Incorporate PaaS
Platform as a service (PaaS) is a cloud computing model where a cloud service provider delivers hardware and software tools to users over the internet versus a build-it-yourself Infrastructure as a Service (IaaS) model. The PaaS provider abstracts everything—servers, networks, storage, operating system software, databases, development tools—enabling teams to focus on their application. This enables PaaS customers to build, test, deploy, run, update and scale applications more quickly and inexpensively than they could if they had to build out and manage an IaaS environment on top of their application. While businesses shouldn’t feel compelled to rewrite all their network configurations and operating environments, they should see where they can have quick PaaS wins to replace aging systems.
Not Proactively Preparing for Cloud Migration
Building a new datacenter is a major IT event and usually goes hand-in-hand with another significant business event, such as an acquisition, or outgrowing the existing datacenter. In the case of moving to a new on-premises datacenter, the business will slow down as the company takes on a physical move. Migrating to the cloud is usually not coupled with an eventful business change, and as a result, business does not stop when a company chooses to migrate to the cloud. Therefore, a critical part of cloud migration success is designing the whole process as something that can run along with other IT changes that occur on the same timeline. Application teams frequently adopt cloud deployment practices months before their systems actually migrate to the cloud. By doing so, the team is ready before their infrastructure is even prepared, which makes cloud migration a much smoother event. Combining cloud events with other changes in this manner will maximize a company’s ability to succeed.
Treating and Running the Cloud Environment Like Traditional Datacenters
It seems obvious that cloud environments should be treated differently from traditional datacenters, but this is actually a common pitfall for organizations to fall in. For example, preparing to migrate to the cloud should not include traditional datacenter services, like air conditioning, power supply, physical security, and other datacenter infrastructure, as a part of the planning. Again, this may seem very obvious, but if a business is used to certain practices, it can be surprisingly difficult to break entrenched mindsets and processes.
How to Plan for a Datacenter Migration
While there are potential challenges associated with datacenter migration, the benefits of moving from physical infrastructures, enterprise datacenters, and/or on-premises data storage systems to a cloud datacenter or a hybrid cloud system is well worth the effort.
Now that we’ve gone over the potential challenges of datacenter migration, how do businesses enable a successful datacenter migration while effectively managing risk?
Below, we’ve laid out a repeatable high-level migration strategy that is broken down into four phases: Discovery, Planning, Execution, and Optimization. By leveraging a repeatable framework as such, organizations create the opportunity to identify assets, minimize migration costs and risks using a multi-phased migration approach, enable deployment and configuration, and finally, optimize the end state.
Phase 1: Discovery
During the Discovery phase, companies should understand and document the entire datacenter footprint. This means understanding the existing hardware mapping, software applications, storage layers (databases, file shares), operating systems, networking configurations, security requirements, models of operation (release cadence, how to deploy, escalation management, system maintenance, patching, virtualization, etc.), licensing and compliance requirements, as well as other relevant assets.
The objective of this phase is to have a detailed view of all relevant assets and resources of the current datacenter footprint.
The key milestones in the Discovery phase are:
Creating a shared datacenter inventory footprint: Every team and individual who is a part of the datacenter migration to the cloud should be aware of the assets and resources that will go live.
Sketching out an initial cloud platform foundations design: This involves identifying centralized concepts of the cloud platform organization such as folder structure, Identity and Access Management (IAM) model, network administration model, and more.
As a best practice, companies should engage in cross-functional dialogue within their organizations, including teams from IT to Finance to Program Management, ensuring everyone is aligned on changes to support future cloud processes. Furthermore, once a business has migrated from a physical datacenter to the cloud, they should consider whether their datacenter team is trained to support the systems and infrastructure of the cloud provider.
Phase 2: Planning
When a company is entering the Planning phase, they are leveraging the assets and deliverables gathered in the Discovery phase to create migration waves to be sequentially deployed into non-production and production environments.
Typically, it is best to target non-production migration waves first, which helps identify the sequence of waves to migrate first. To start, consider the following:
Mapping the current server inventory to the cloud platform’s machine types: Each current workload will generally run on a virtual machine type with similar computing power, memory, and disk. Oftentimes though, the current workload is overprovisioned, so each workload should be evaluated to ensure that it is migrated onto the right VM for that given workload.
Timelines: Businesses should lay out their target dates for each migration project.
Workloads in each grouping: Figure out what migration waves are grouped by i.e. non-production vs. production applications.
The cadence of code releases: Factor in any upcoming code releases as this may impact the decision of whether to migrate sooner or later.
Time for infrastructure deployment and testing: Allocate adequate time for testing infrastructures before fully moving over to the cloud.
The number of application dependencies: Migration order should be influenced by the number of application dependencies. The applications with the fewest dependencies are generally good candidates for migration first. In contrast, wait to migrate an application that depends on multiple databases.
Migration complexity and risk: Migration order should also take complexity into consideration. Tackling simpler aspects of the migration first will generally yield a more successful migration.
As mentioned above, the best practice for migration waves is to start with more predictable and simple workloads. For instance, companies should start with migrating file shares first, then databases and domain controlled, and save the apps for last. However, sometimes the complexity and dependencies don’t allow for a straightforward migration. In these cases, utilizing an experienced service provider who has experience with these complex environments will be prudent.
Phase 3: Execution
Once companies have developed a plan, they can bring them to fruition in the Execution phase. Here, businesses will need to be deliberate about the steps they take and the configurations they develop.
In the Execution phase, companies will put into place infrastructure components and ensure they are configured appropriately, like IAM, networking, firewall rules, and Service Accounts. Here is also where teams should test the applications on the infrastructure configurations to ensure that they have access to their databases, file shares, web servers, load balancers, Active Directory servers, and more. Execution also includes using logging and monitoring to ensure applications continue to function with the necessary performance.
In order for the Execution phase to be successful, there needs to be agile application debugging and testing. Moreover, organizations should have both a short and long-term plan for resolving blockers that may come up during the migration. The Execution phase is iterative and the goal should be to ensure that applications are fully tested on the new infrastructure.
Phase 4: Optimization
The last phase of a datacenter migration project is Optimization. After a business has migrated its workloads to the cloud, it should conduct periodic reviews and planning to optimize the workloads. Optimization includes the following activities:
Resizing machine types and disks
Leveraging software like Terraform for more agile and predictable deployments
Improving automation to reduce operational overhead
Bolstering integration with logging, monitoring, and alerting tools
Adopting managed services to reduce operational overhead
Cloud services provide visibility into resource consumption and spending, and organizations can more easily identify the compute resources they are paying for. Additionally, businesses can identify virtual machines they need or don’t need. By migrating from a traditional datacenter environment to a cloud environment, teams will be able to optimize their workloads due to the powerful tools that cloud platforms provide.
How do I take the first step in datacenter migration?
While undertaking a full datacenter migration is a significant project, it is worthwhile. The migration framework we’ve provided can help any business break down the process into manageable stages and move fully to the cloud.
When you’re ready to take the first step, we’re here to help to make the process even easier. Contact a 2nd Watch advisor today to get started with your migration to the cloud.
