Data lake consulting helps you design, build, migrate and govern one central store for all your structured, semi-structured and unstructured data, on AWS, Azure, Databricks or Microsoft Fabric.
A data lake is one central store that keeps all your data (tables, logs, files, images and events) in its original format at low cost. Teams can clean it, query it and feed it to dashboards and AI models without first copying it into separate systems. It isn’t an ETL tool: pipelines load data into the lake, and the lake holds it.
Most companies end up with a lake, a warehouse and a pile of spreadsheets that each show a different number. Data with no owner turns the lake into a swamp, analysts spend their week hunting for the right file, and AI projects stall on data nobody trusts. A consultant fixes the foundation first: one governed platform, clear data ownership, and pipelines that keep data clean as volumes grow.
Therefore, it is really important that you take actions to prevent your business from moving forward. Waiting for the next crisis to sort your data out is how a small problem turns into an expensive one. The work costs less before you need the answer than after.
You need a data lake when your team puts in efforts to hunt down data from isolated silos rather than analysing it. Every hour spent chasing a file across systems is an hour nobody spends asking why the numbers moved. One landing place ends the hunt.
You have to keep systems ready so that you can outrun your slow reporting systems and feed raw information into modern AI tech. Older stacks were built for monthly cycles, not for a question asked on a Tuesday afternoon. Your models need the raw data too, in a form they can actually read.
If you cannot access your data insights, ultimately your business growth will suffer. You have already paid to collect and store all of it. An insight you cannot reach in time counts the same as one you never had.
Our track record involves providing data lake consulting services across countries like US, UK, Australia. We offer services across a full lifecycle; starting from strategy, architecture, implementation, migration, and long-term managed operations. It is important for us to know your intent regarding the pool of data you have. This will assist us to make you reach your goal easily.
We start our process by understanding your current system, compliance obligations, and the end goal for your data. We compare Apache Iceberg, Delta Lake, and Apache Hudi against your existing data ingestion patterns. Finally, we create a strategy personalised for your business, that helps you track and manage data with safety and ease.
We design and build secure data lakes that can easily grow alongside your business. Whether you prefer to work on AWS, Microsoft Azure, or Google Cloud, we use the smartest built-in tools each platform has to offer. Most importantly, we connect your data directly to your business dashboards, right from the start.
We add warehouse-grade tables, transactions and governance on top of your lake with Databricks, Snowflake or Microsoft Fabric and open formats like Apache Iceberg and Delta Lake. Databricks usually suits heavy ML and AI work, Snowflake suits SQL-first analytics teams, and Iceberg suits teams that want to switch engines later.
We build your data lake ready to feed to your AI and ML. You will not have to waste time creating separate prep steps for every new model you want to run. We help you to position smart tools that can predict what your customers want, and forecast your future demands.
We move on-prem Hadoop and Cloudera clusters, file shares and legacy warehouses to Amazon S3, Azure Data Lake Storage, Databricks or Microsoft Fabric OneLake, using open table formats like Apache Iceberg and Delta Lake. We don’t move everything at once: a small batch goes first, and the rest follows only after that batch runs correctly.
We focus on the foundations of your data to ensure that it stays fast and reliable as your business grows. We don’t believe in feeding your data into generalised templates. Every business is different and therefore, we personalise and custom-engineer your storage systems to handle the bandwidth of data your business needs.
At Algoscale, we build your data system from scratch. We organise data for you that helps you connect with your dashboards, smart tools and softwares you already use. We create systems that help you in the long-term.
We handle the complete setup for you, starting from the daily development, and all the behind-the-scenes maintenance needed to keep your system running at top speed. Enterprise-level data capability without hiring and training your own internal IT team.
| Moving from | Moving to | What we convert |
|---|---|---|
| Hadoop / Cloudera (HDFS, Hive) | Amazon S3, Azure Data Lake Storage, Databricks | Hive tables, Spark and MapReduce jobs, Oozie workflows |
| On-prem file shares and SFTP drops | Amazon S3, Azure Data Lake Storage | Folder structures, file formats, ingestion schedules |
| Legacy data warehouses (SQL Server, Oracle, Teradata) | Databricks, Microsoft Fabric | Tables, ETL jobs, reporting feeds |
| Azure Synapse / ADLS Gen1 | Microsoft Fabric OneLake | Pipelines, Spark notebooks, Power BI datasets |
Swipe the table sideways to see every column.
Results from data lakes and lakehouses Algoscale has put into production.
ECM Energy Services went from kickoff to production go-live in 14 weeks, with NetSuite, Fleetio, Samsara, KPA Flex and ADP landing in one medallion data lake on Azure Data Lake Storage Gen2.
Juice Ventures replaced weekly manual report pulls with real-time dashboards on one governed Databricks platform, cutting manual effort by 80%.
Moving reporting from Power BI to Looker and streamlining data operations cut Juice Ventures’ reporting infrastructure costs by 40%.
Algoscale delivers data lake consulting across field services, financial services and digital lending. Results include 80% less manual effort, 30% faster loan processing and 5X faster loan approvals.
Built a medallion data lake on Azure Data Lake Storage Gen2 with Azure Data Factory, unifying Oracle NetSuite, Fleetio, Samsara, KPA Flex and ADP, governed with Microsoft Purview and served to 8 Power BI modules.
Brought HubSpot, Azure SQL and Amazon S3 data into Databricks with Unity Catalog governance for the London lender, replacing weekly manual report pulls with real-time Looker dashboards.
Built metadata-driven ETL from Amazon S3 with AWS Step Functions, with self-hosted Apache Superset dashboards replacing paid BI licenses.
I’ve been tremendously impressed by their knowledge, skills and professionalism.
Neeraj and Algoscale enabled Perceptronix and my clients have the cutting edge solutions they need to solve the very real problem that they have. We really enjoy working with their development team — our projects are always well defined and managed by project leaders.
We are impressed with their good communication skills.
Algoscale Technologies, Inc. provided a transportation company with BI, big data consulting, and SI services. The team was tasked with improving the client’s traffic movement counts at several intersections.
We are extremely happy with the work that they’ve done.
They are responsive, the quality of the engineers and data scientists are very very good. They are challenged by us and ultimately always deliver. We find that the management team are really attuned to the kind of skills that we need.
…Algoscale is unwilling to settle for anything less than full customer satisfaction.
Algoscale Technologies, Inc. created an engine to capture data and an analytics platform to synthesize the information. They consulted on which technologies to use and provided maintenance.
100% of the deadlines set by Algoscale Technologies, Inc. have been met.
Algoscale Technologies, Inc. provides ongoing analytic, development, and data science support for an analytics firm.
Five failure patterns behind data lakes that stopped being trusted — discovery, ownership, schema drift and AI exposure — with corrective patterns for zones, governance, contracts and recovery, and a straight answer on whether to fix or rebuild.
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The same data can serve three different purposes, depending on what you need from it. A warehouse keeps structured data ready for fast, reliable reporting, while a lake stores raw data cheaply for analytics and AI. A lakehouse brings those approaches together, so you can work with both from the same data.
| Data Warehouse | Data Lake | Data Lakehouse | |
|---|---|---|---|
| What it stores | Structured, modelled data | Raw, unstructured and structured data at scale | Raw data with warehouse-style tables on top |
| Schema | On write - modelled before it lands | On read - structure applied when queried | On read, with enforced table schemas where needed |
| Best for | Reporting and business intelligence | Big data, data science and advanced analytics | BI and AI/ML on one copy of the data |
| Cost profile | Higher per TB, compute-optimised | Low-cost object storage | Object storage plus a table layer |
| Governance | Strong, built in | Needs to be designed in deliberately | ACID transactions, lineage and time travel |
| Typical tech | Snowflake, Redshift, BigQuery, Synapse | S3, ADLS, GCS | Apache Iceberg, Delta Lake, Apache Hudi |
A five-stage engagement: we frame the problem against your business goals, orchestrate the architecture, run batch and real-time pipelines at scale, embed governance and audit readiness, then keep tuning performance and cost long after go-live.
We start by aligning on what truly matters: your business goals, data challenges, and current limitations. This includes assessing your existing systems, identifying bottlenecks, and defining clear success metrics before any architecture decisions are made.
You get: a written assessment of your sources, goals and success metrics, with a platform recommendation.
Next, we design a scalable, future-ready architecture tailored to your needs. Whether it’s a data lake, or lakehouse approach, we orchestrate the right combination of tools, platforms, and data flows to ensure performance, flexibility, and governance.
You get: the target architecture, a table format choice and a migration plan sequenced by source.
We build and deploy robust data pipelines that handle both batch and real-time workloads. From ingestion to transformation, everything is engineered for reliability, speed and consistency, ensuring your data is always ready when you need it.
You get: production batch and streaming pipelines with quality checks, plus reconciliation reports.
We embed governance, security, and compliance into the foundation. With strong access controls, data lineage, quality checks and audit readiness, your platform stays secure, compliant, and trustworthy as it scales.
You get: an access-control matrix, lineage and audit logging, documented in your catalog.
A data platform isn’t static. We continuously monitor performance, optimize costs, and refine architecture based on real usage patterns, ensuring your system keeps improving as your data and business grow.
You get: monitoring and alerts, plus regular cost and performance reviews.
Our expertise, experience and unparalleled support makes us stand out from other data lake consultants
Over 12 years of experience helping businesses solve complex data, analytics, AI, and software challenges.
150+ projects delivered across industries, solving diverse business challenges from data modernization to analytics and AI.
Build data lake solutions with security and governance aligned to HIPAA, PCI-DSS, SOX, GDPR, and ISO 27001 requirements.
Get end-to-end support across data strategy, governance, engineering, warehousing, lakehouses, analytics, and AI from one team.
Get support before and after delivery, including ongoing fixes, enhancements, monitoring, alerts, and new requirements, backed by SLAs.
Recognized by Clutch as a Champion and Global 2025 winner, with 5-star ratings and recognition among leading data analytics companies.
The platforms we build on, migrate between, and optimise daily.
Data lake engagements at Algoscale are led by Neeraj Agarwal, Architect & Practice Lead, and Tanmay Agrawal, Solutions, Data.
Get clear answers on what’s included, which platforms we work with, migration, ongoing support, and how data lakes compare with warehouses.
5.0 / 5 · 12 reviews
ISO 27001
Clutch Champion 2025
Clutch Global 2025
Best Data Analytics Companies 2025
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