Service Overview
To stay on the right side of the growth curve, businesses today rely heavily on Analytics and Planning tools. TekLink has immense expertise with EDW Implementation, Modern Data Warehouse, Enterprise Data Lake, AMS Support, Visualization technologies, and Enterprise Data Lake Strategy to get the most out of them. Businesses can leverage TekLink's knowledge to improve their decision-making. TekLink’s AMS Services also help store and integrate disparate data on one platform and uses it for various purposes such as Advanced Analytics, Ad-hoc Analysis, and Self-Service.
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Service Offerings
With TekLink's industry-leading service offerings, businesses can improve their analytical capabilities and have a significant impact on the industries they serve, allowing them to become industry influencers.
With TekLink’s industry-leading service offerings, businesses can improve their analytical capabilities and significantly impact the industries they serve, allowing them to become industry influencers.
TekLink’s consultants help businesses implement their Analytics solution on SAP and other heterogeneous data with different cloud solution providers such as Azure, AWS, Google Cloud Platform (GCP), and SAP Cloud. TekLink accomplishes this goal through having a complete understanding of SAP data and applications, such as SAP ECC and SAP S/4 HANA, among others.
TekLink’s consultants guide businesses to transform their infrastructure into a Modern Cloud Platform for Analytics. TekLink consultants help create a data foundation for Analytics on the Cloud Platform by integrating from different source systems, such as Oracle NetSuite, Priority, Epicor, Dynamics NAV, and many others.
TekLink’s Application Management Services (AMS) continuously strives to enhance technology’s value to businesses. It is made possible by TekLink’s Global Delivery Center, which delivers various services under its AMS umbrellas, such as monitoring, alerts, cost optimization, upgrades, and more.
Predictive analytics and modeling are now more accessible than ever before because of the remarkable convergence of intuitive tools, innovative predictive algorithms, and hybrid cloud deployment methods. With TekLink’s Big Data and Advanced Analytics capabilities, businesses of all sizes can integrate advanced analytics into their operations and use AI at a large scale.
With TekLink’s extensive experience in implementing complete Analytics Solutions on Modern Cloud Platforms, businesses can make smarter business decisions, allowing them to stay ahead of the curve. TekLink’s Implementation Services include Data Integration, Organization, Enrichment, Harmonization, Governance, Modeling, Dashboards, and others. Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform, and SAP Cloud are TekLink’s expertise.
Self-service analytics is becoming increasingly popular among enterprises. Client teams can collaborate with TekLink to implement data discovery and visualization tool options. Delivering the necessary training and education to client teams is a part of the process.
Data is at the heart of any organization, and it is only getting more important to gain a competitive advantage. Given the exponential growth of data and its sources, TekLink assists in implementing data integration from multiple sources and formats using integration technologies for business gain.
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Frequently Asked Questions
Here are the steps that go into implementing an Enterprise Data Warehouse:
- Determine Business Objectives
- Collect and Analyze Information
- Identify Core Business Processes
- Construct a Conceptual Data Model
- Locate Data Sources and Plan Data Transformations
- Set Tracking Duration
- Implement the Plan
Here is how an enterprise data lake can be built:
- Set up storage
- Move data
- Cleanse, prepare, and catalog data
- Configure and enforce security and compliance policies
- Make data available for analytics
With Enterprise Data Lake services, you can democratize data access and avoid developing silos of data.
To ensure success while implementing a data lake, four key considerations must be made:
- When sourcing data for the data lake, schema and data quality must be prioritized so that data consumers can use the data lake.
- Data users must be able to use their preferred tools, such as Tableau, Python, and R.
- Once insights have been gained, the process must be streamlined so that it is ready for enterprise-level outputs, which often necessitate data preparation or transformation, a data catalog or semantic layer, query acceleration, robust data integration, and data governance.
- Data consumers require ad hoc querying, low latency, high concurrency, workload management, and integration with BI tools.
A data lake is a sizable collection of unprocessed data, the use of which is currently unknown. A Data Warehouse structures, filters, and stores data that have already been processed for a particular purpose. Data lakes are used by data scientists, are highly accessible, and are easy to upgrade. Data warehouses, however, are used by business professionals, are more complicated, and are costlier to make changes.
The distinction between data lake and data warehouse is crucial because they have different functions and must be properly optimized by various viewpoints. A data warehouse will better fit another company, while a data lake works for one.
A data lake is, by design, centralized. However organizations don’t need to centralize all of their data in a single Lake. Many Lake-based organizations use a decentralized approach to data storage and processing but a centralized approach to security, governance, and discovery.
Data science can be easily applied to a variety of business issues because some applications don’t even need any programming or technical know-how. However, if properly applied, the same fundamental approaches might be used in larger systems on a far larger scale. To guarantee that projects are successful, you must carefully prepare how you will include databases, business logic, algorithms, and new regulations.