Today, organizations are increasingly investing in new cloud-based platforms, processes, and environments to exploit benefits such as scalability, flexibility, agility, and cost-efficiency. Concurrently, organizations also acknowledge that data management is the initial step to successful digital transformation. With automated cloud integration services, you gain the ability to unite your data sources and drive important insights quickly.
When you put these trends together, IT departments are employed to help the business become cloud-ready, to modernize analytics. Enterprises are modernizing or adopting new data warehouses and data lakes in the cloud environment. In one cloud data platform, you have a mutual solution for both historical and predictive analytics.
However, when it is a matter of managing the data to speed up the value and bring ROI with an investment in cloud data warehouses, lakehouses, and data lakes, the usual approach that IT departments tend to choose, can have major implications like increased cost, project overruns and maintenance intricacy removing any benefits of modernizing analytics in the cloud.
Challenges in a Multi-Cloud and Hybrid World for Data Management
As IT companies begin sustaining cloud and analytics or AI projects, the inducement is to accuse their technical developers of designing, developing, and deploying the right solution. However, they hurriedly get into data challenges if they fall into the hand-coding path. In a lot of cases, these complexities exploit on-premises data warehouses and data lakes:
Varied and siloed data:
Many organizations have different types of data available in many dissimilar systems and storage formats, either on-premises or in the cloud. The data is every so often distributed throughout siloed data warehouses, data lakes, cloud applications, or third-party assets. However, more data is created from online transaction systems and communications like web and machine log files and social media. For instance, in a retail firm, data is dispersed across numerous different systems. These systems include point of sale (POS) systems, including in-store transaction data, customer data in a CRM and MDM system, social and web click-stream data accumulated in a cloud data lake, and more. ( Cloud adoption )
Lack of data governance and quality:
Varied and siloed data often change the values of data quality and governance. Policies are hardly ever enforced constantly. Data is discarded into data lakes creating swamps where data is hard to search, understand, manage, and defend. Even inferior is soiled data approaching a cloud data warehouse, where multiple business analysts and other data users rely on it for decision-making, predictive analytics, and AI.
A Lot of Emerging and Changing Technologies:
As the amount of data is increasing, new vendors, technologies, and open-source projects are coming into effect that change the IT environment. There are traditional, new, and evolving technologies available for computing, storage, databases, applications, analytics, and even new AI and machine learning. Developers may efforts to stay on top of this varying environment, making it complicated to standardize or execute a methodology.
Why are some organizations still using hand-coding?
There are still some organizations that choose hand-coding, supposing that it’s an easier approach than deploying a data integration tool, which may require some level of skills and knowledge. In addition to this, developers may think that integration tools can limit their creativity for a custom use case and practice. In many cases, these are some short-sighted doubts about a smart and automatic data solution. However, hand-coding may be suitable for faster proofs-of-concept (POC) with a low-priced entry.
Disadvantages of Hand Coding in IT
Initially, IT departments may find hand-coded data integrations as a fast, economical way to construct data pipelines. But there are important disadvantages to consider.
Hand Coding Is Costly
In due course, hand-coding is costly to execute, operate, and maintain production. Hand coding needs to be edited and optimized from growth to consumption. And with large IT budgets in operations and maintenance processes, the cost of hand-coding increases with time.
Hand Coding Is Not Long-Term
With new and emerging technologies, developers have to re-structure and recode whenever there is a technology change, an upgrade, or even a modification to the primary processing engine.
Hand Coding Lacks Automation
Hand-coding doesn’t extend to data-driven organizations and can’t maintain speed with enterprise requirements. There are too many requirements for data integration pipelines for IT users to contain. The only way to range the delivery of data integration projects is through automation, which needs AI and machine learning.
Hand Coding Lacks Enterprise Width
It took many years for data integration hand coders to understand how essential data quality and governance are to make sure the business has reliable data. It is even more significant for data-driven companies for the development of AI and machine learning. Hand coding can’t present enterprise width for data integration, metadata management, and data quality.
Disadvantages of Hand-Coding for Businesses
The limitations of hand-coding aren’t limited to IT only. Eventually, hand-coding influences overall business outcomes. Here are the following key areas where hand-coding can have a harmful business impact:
- Higher Cost
- More Risks
- Slower Time to Value
Create that Illuminating Moment with Cloud Data Management
After struggling for months in the initial modernization project, Informatica realized the need to re-evaluate its cloud data management strategy. By reconsidering the drawbacks of hand-coding, they improved their strategy to decrease manual work and make efficiency better through automation and scaling. Businesses require a cloud data management solution that comprises:
- The facility for both business and IT users to recognize the data ecosystem, through an ordinary enterprise metadata establishment that presents end-to-end lineage and visibility throughout all environments
- The capacity to reuse business logic and data conversion, which increases developer productivity and allows business stability as it encourages integrity and uniformity of reuse
- The capability to conceptualize the data transformation logic from the primary data processing engine, which will make it long-lasting under the quickly changing cloud environment
- The capability to connect to an assortment of sources, targets, and endpoints without any requirement for specialized code connectivity
- The ability to process data competently with an extremely performant, scalable, and dispersed server-less data processing engine or the capacity to control cloud data warehouse pushdown optimization
- The ability to work and continue data pipelines with the least interruptions and cost
Components of Smart, Automatic Cloud Lakehouse Data Management
As organizations are joining and modernizing their on-premises data lakes and warehouses in the cloud or building up new ones in the cloud, it has become more important than ever to escape from the drawbacks of hand-coding. Especially, today, with the evolution of Lakehouse is presenting the best of data warehouses and data lakes that come with cloud agility and flexibility. So it’s important to adopt metadata-driven intelligence and automation to create efficient data pipelines.
While many IT departments only focus on data integration, a more enhanced solution is required to solve today’s enterprise needs across the complete lifecycle of data management. Here are four main components required in the data management strategy:
Data Integration
A best-in-class intelligent, automated data integration solution is necessary to manage cloud data warehouses and data lakes. Below are a few functionalities that allow you to rapidly and competently build data pipelines to send into your cloud storage:
- Codeless integration with templates, suggested by AI for next-best transformations
- Group ingestion of files, databases, changed data, and streaming
- Pushdown optimization of databases, cloud data warehouses, and PaaS lakehouses
- Serverless and expandable scaling
- Spark-based functions in the cloud
- Large and native connectivity
- Stream processing
- AI and machine learning growth to handle schema drift and complicated file parsing
- Support for data and machine learning processes (DataOps and MLOps)
Data Quality
Nowadays, with the development of cloud lakehouses, it’s not sufficient to encompass top-class data integration. You also require best-in-class data quality. The smart, automated data quality features ensure that data is cleansed, consistent, trusted, and standardized across the enterprise. Here’s what you should look for:
- Data profiling integrated with data governance
- Data quality policies and automated rule creation
- Data dictionaries to manage lists of values
- Cleansing, parsing, verification, standardization, and de-duplication processes
- Integration with your data integration tool
- Data analytics for quality
- Spark-based functioning in the cloud
Metadata Management
A general enterprise metadata establishment allows smart, automated, point-to-point visibility, and extraction across your environment. Wider metadata connectivity throughout different data types and sources makes sure that you have visibility into it and can use data kept protected in varied transactional applications, data stores and systems, SaaS applications, and custom legacy systems. An ordinary enterprise metadata structure enables smart, automated:
- Data discovery
- End-to-end lineage
- Value tagging and data curation
- Perception of technical, business, functional, and traditional metadata
- Connectivity through on-premises and cloud for various databases, apps, ETL, BI tools, and other systems
Cloud-Native Features Built on a Base of AI and Machine Learning
This component is foundational and performs under the other three. The components of data integration, data quality, and metadata management need to be developed on the basis of AI and machine learning to manage the exponential growth in organizational data. Always pick up the cloud-native solution that is multi-cloud, API-driven, and microservices-based and also look for the following features in it:
- AI/ML-driven automation, like next-best transformation suggestions, data pipeline resemblance, operational notifications, and auto-tuning
- Containerization
- Server-less architecture
- Minimum install and setup
- Auto-upgrades
- Usage-based rates
- Trust certifications
- Integrated full-stack high accessibility and superior security
Take a Comprehensive Approach to Smart, Automatic, and Modern Cloud Data Management
Many organizations require data to understand, process, and grow their business effectively, but data complexity is an obstruction. IT companies are searching for an intelligent, automatic data management solution that fills the space between on-premises and cloud deployments without requiring rebuilding everything from the start before they can garner the benefits of successful execution.
Without a united and wide-ranging data platform, organizations are required to exploit different point solutions that were never intended to work together initially. It takes immense time to integrate these systems, which is also very expensive, risky, and inflexible to be amended later. If there is any change in one point of the solution, then you have to repeat and retest all integrations in the system.
You don’t need a big-bang implementation to take an enterprise method. One of the major benefits of having intelligent and automated data management is that companies can compress the use of general methodologies, processes, and technologies increasingly, starting with one or two projects initially.
By choosing an enterprise data management platform for high productivity, IT teams can speed up start-up projects to bring instant business value. As the IT companies implement supplementary projects, they can exploit and reuse available assets, considerably decreasing the cost and time to bring new capabilities to the business and making consistency and control better.
With the leading metadata-driven cloud data management solutions in the industry, you get the power to leverage the complete features of your cloud data warehouse and data lake across a multi-cloud, hybrid ecosystem. You can boost efficiency, ensure more savings, and start small initially, and scale with top-in-class data integration tools for the cloud, on an AI-driven, intelligent data management platform.
Summary
As you know, data is a valuable asset for businesses. So when you run a business on a large scale, hand-coding can bring a lot of manual errors. The IT department cannot suitably take care of your data management, quality, governance, and security, and derive insights quickly that also need to be actionable. Therefore, an automated data management solution is a smart option to start managing your data intelligently.
Are you worried about bringing value to your business’s most important asset, data? Rise above the manual coding and choose an automated approach with professional Data Integration Services that will help you to exploit cloud capabilities for your databases. ExistBI has consulting teams in the United States, the United Kingdom, and Europe.
