Comprehensive Data Lake Consulting Services

We are a leading data lake development company helping organizations turn raw data into actionable insights through secure, scalable, cost-efficient data lake solutions.

Benefits of implementing a data lake in your organization

Improved data accessibility

It is incredibly flexible, enabling users with different skills, tools, and languages to access data and perform various analytics tasks faster, improving informed decisions.

Centralized data storage

Centralizes your data, eliminating data silos problems like data duplication, multiple security policies, and difficulty with collaboration, offering downstream users a unified view of all sources of data.

Increased business agility

React quickly to market changes with a flexible data foundation, ensuring adaptability to evolving scenarios that enable you to gain an edge over the competition.

Cost-efficient solutions

Optimize data storage and processing costs with efficient data management to maximize ROI and minimize expenses.

Our data lake consulting services

Data lake strategy and design

Our consultants understand your business goals and use proven methodologies to develop a robust data lake strategy that aligns with your objectives. We seamlessly integrate your existing IT infrastructure to support business growth.

Data ingestion and integration

Efficiently gather and load data from various sources, including ERP, CRM, databases, APIs, and IoT devices. Our experts handle both structured and unstructured data, optimizing batch and real-time ingestion for maximum efficiency and accessibility.

Data lake implementation

Following our meticulously crafted data lake architecture blueprints, our expert consultants implement data lake solutions that help convert raw data into actionable insights for informed decision-making, adhering to industry best practices.

Data security & governance

To safeguard your data, we implement robust data security measures, access controls, and encryption. Our governance framework ensures compliance with industry regulations for on-premise, hybrid, and cloud environments.

Our approach to data lake consulting services

Phase 1: Discovery and assessment

We analyze your current data infrastructure, identify key opportunities for improvement, and lay the structure for a successful new data lake project.

Phase 2: Data lake architecture design

We develop a scalable and robust data lake architecture. Implementing one will ensure you transform complex data into competitive value and drive measurable results.

Phase 3: Implementation and Integration

We build and test the data lake to ensure seamless integration and functionality and deploy a robust data lake solution.

Phase 4: Ongoing management and optimization

After the deployment of the data lake solution, we do not stop there. We provide ongoing training and support to help you maximize your data lake investment.

Why choose ExistBI for your data lake consulting services?

Proven data expertise

With decades of experience providing Data solutions, our data lake specialists offer unmatched expertise in implementing data lake solutions.

Customized solutions

Thanks to our consultants’ deep expertise in database technology, we offer tailored data lake strategy solutions and tools to help you meet your goals.

End-to-end support

300+ professionals across North America, the UK, and the European Union are ready to support you throughout the entire data lake lifecycle.

Cutting-edge technology

We work with leading data lake platforms and technologies to develop the most reliable hybrid and cloud solutions for your unique requirements.

Data Lakes Case Studies

U.S. Air Force – Data Management, Data Governance & Informatica Expertise

Recent Case Studies, BI Analytics Case Studies, Big Data Case Studies, Data Governance Case Studies, Data Lake Case Studies, Data Quality Case Studies, Data Science Case Studies, Databricks Case Studies, Industry: Public Sector and Government, Informatica Case Studies, Machine Learning Case Studies, Predictive Analytics Case Studies, Tableau Case Studies

Read why ExistBI was shortlisted by the U.S. Air Force’s to support the ambitious VAULT data platform project.

Process Insights – Complex Cross-Border Cloud Data Warehouse with Azure SQL and Power BI Case Study

Recent Case Studies, BI Analytics Case Studies, Business Intelligence Case Studies, CRM Case Studies, Data Integration Case Studies, Data Lake Case Studies, Data Quality Case Studies, Data Science Case Studies, Data Warehouse Case Studies, ERP Case Studies, Industry: Manufacturing, Industry: Retail, Machine Learning Case Studies, Microsoft Azure Case Studies, Microsoft Case Studies, Microsoft Power BI Case Studies, Predictive Analytics Case Studies, Regulatory Compliance Case Studies, Salesforce Case Studies, Support Services Case Studies

Read how ExistBI designed a tailored solution that will give unprecedented insights into the company’s operations across international locations.

Boyd – Data warehouse delivers unified view and decision-making

Data Warehouse Case Studies, BI Analytics Case Studies, Big Data Case Studies, Business Intelligence Case Studies, CRM Case Studies, Data Governance Case Studies, Data Integration Case Studies, Data Lake Case Studies, Data Migration Case Studies, Data Quality Case Studies, Data Science Case Studies, ERP Case Studies, Industry: Manufacturing, Informatica Case Studies, Machine Learning Case Studies, MDM Case Studies, Microsoft Azure Case Studies, Microsoft Case Studies, Microsoft Power BI Case Studies, Predictive Analytics Case Studies, Recent Case Studies, Regulatory Compliance Case Studies, Salesforce Case Studies, Support Services Case Studies, Tableau Case Studies

Learn how ExistBI designed a data warehouse using MS SQL Server, Informatica and Power BI for complete view of global operations.

FAQs

What are data lakes?

A data lake is a centralized cache that stores huge amounts of raw data in its native format. It enables efficient data collection, storage, management, and analysis, fostering insights that propel strategic decision-making and process optimization for your business.

Data lakes vs data warehouses, what is the key difference?

A data lake is a large pool in which every data type (structured, unstructured, or semi-structured) is stored. Data lakes are scalable, can act as a kind of cache for data warehouses, and are a low-cost way to store files in any format. This is particularly attractive for less structured data such as documents, images, emails, and audio files.

On the other hand, data warehouses are information bases for traditional company reports or audit assessments in medium and large companies. Structured data collected days, weeks, or even months ago is often prepared and analyzed in an ETL (Extract, Transfer, and Load) process.

Both have their advantages and disadvantages, and which one to choose is dependent on your application.

To learn more, read: What Is The Difference Between Data Lake And Data Warehouse?

Is the data lake taking the place of the data warehouse?

Data lakes are not replacing data warehouses; instead, they complement each other. Data lakes are more suitable for ingesting and storing raw and unstructured data at any scale. In contrast, data warehouses are designed for structured data and optimized for reporting and analytical queries. Many organizations use data lakes and warehouses to meet their diverse data storage and analysis needs.

What is the cost of setting up a data lake?

Costs vary based on complexity, scale, and chosen technology. We provide tailored quotes to meet your specific needs. Nevertheless, the price can range between 200K and 1M USD.

How do you ensure data security and compliance in a data lake?

We implement robust security protocols for encryption, restrict access control, and keep systems up-to-date to protect against viruses and hacking, safeguard your data, and adhere to industry standards.

What industries do you serve with your data lake consulting services?

Since 2008, we have helped Small and mid-size companies, large organizations, and Fortune 500 companies across 20+ industries, including healthcare, banking and finance, retail, manufacturing and supply chain, and Telecoms, consolidate disparate data into highly automated, scalable data lake and data warehouse solutions that enable timely, advanced analytics and streamline enterprise-wide decision-making.

Can you help with migrating existing data to a new data lake?

Yes, we provide data migration services as part of our data lake implementation.

How long does it typically take to implement a data lake solution?

The time it takes to implement a data lake solution depends on the project’s scope and complexity. Typically, it takes between a few weeks to a year to deploy. Nevertheless, we work efficiently to meet your deadlines.

What ongoing support do you provide after the implementation of a data lake?

Our obligation to your success continues even after implementation is completed. Our data lake experts and highly experienced data strategy team are ready to address any issues, update software as required, and enhance your business as it evolves.

How do you approach data governance within a data lake environment?

We implement structured governance frameworks to maintain data quality, security, and compliance.

What tools and technologies do you recommend for managing a data lake?

Managing a data lake requires a combination of tools and technologies to ensure data quality, security, and accessibility. Here are some recommendations:

Informatica, Tableau, SAP BusinessObjects, ELT (Extract, Load, Transform), Azure Data Lake Storage, Google Cloud Storage, Apache Spark, S3/Lake Formation, Apache NiFi, Flume, and classic ETL technologies such as Talend and Microsoft SSIS, etc.

Note that the specific tools and technologies may vary depending on your organization’s needs and existing infrastructure.

How do you measure the success of a data lake implementation?

We partner with your data scientists and engineers to define key performance indicators (KPIs) aligned with your strategic objectives. These metrics could include:

We track these KPIs throughout the data warehouse lifecycle and provide ongoing reporting to demonstrate the solution’s effectiveness and impact on your business goals.