We understand your business can’t wait for clear, actionable data and insights. Our data lake consultants will help you with data management and cloud migration. The result? Faster, trusted data to drive your critical business processes.
Implement a powerful data lake to advance your analytics and insight discovery capabilities.
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Implementing or upgrading an enterprise data warehouse is one of the most important projects your business will embark on. Leverage our data lake architecture blueprints.
Become a data-driven organization with the meaningful insights required to make business decisions immediately.
Migrate your data into a cloud to take advantage of the world class data infrastructure and data security.
Unlock the full potential of the data lake architecture. Our data lake consultants are experienced in implementing data lake solutions.
Automate complex data management and cloud migration efforts to access data faster to drive your critical business decisions with data.
Data warehouse consulting services use Agile methodology to speed up strategic planning and data lake implementation.
Become a data-driven organization with the advanced analytics and insights required to make data-driven decisions.
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Java-based engine to synchronize data from a relational OLTP database into a scalable AWS Redshift OLAP warehouse for more efficient queries processing
Using Apache Kafka to enhance an existing Apache Spark software system, increase the efficiency of property market analysts work and realize substantial data handling costs savings.
The sheer number of data sources in a modern enterprise environment, combined with the challenges of storing, processing, and accessing both structured and semi-structured data, has driven demand for sophisticated data warehouse solutions.
A data lake is a centralized repository that allows you to store all your raw data, structured and unstructured data at any scale. It can store data in its native format and process any variety of it, ignoring data storage limits.
Companies today are also starting to look at the value of data lakes. An Aberdeen survey saw organizations that implemented a data lake outperforming similar companies by 9% in organic revenue growth.
With the data lake solution, business users are gaining a deeper understanding of business situations as they have more context than ever before, allowing them to accelerate analytics experiments.
A cloud data lake is a cloud-hosted centralized repository that allows you to store all your structured and unstructured data at any scale. Data lakes are usually considered complementary solutions to data warehouses.
The most popular cloud providers, Amazon, Google, and Microsoft, all offer cloud data lakes and data warehouses:
Amazon Web Services
AWS Lake Formation allows you to create a secure data lake in days. In a data lake, all your data is centralized, curated, and ready for analysis. Amazon Redshift allows you to run complex analytic queries against petabytes of enterprise data. And with Amazon QuickSight, you can create stunning visualizations and rich dashboards that can be accessed from any browser or mobile device. AWS Glue service can be used to perform data transformation. AWS Athena can be used to analyze data stored in AWS S3.
Google Cloud Services
Google Cloud Storage (GCS) is a lower-cost cloud data lake. On top of that, the Google BigQuery solution offers an enterprise data warehouse for analytics. The serverless solution creates a logical data warehouse from managed columnar storage, object storage, and spreadsheets. BigQuery uses streaming ingestion to capture data in real-time and runs on the Google Cloud Platform. Users can also share data, queries, spreadsheets, and reports.
Microsoft Azure Cloud
Azure Data Lake Store (ADLS), is a hyper-scale repository for an enterprise data lake. It enables developers, data scientists, and analysts to store, process, and analyze data of any size, shape, or speed across platforms and languages. In addition, it integrates with operational stores and data warehouses.
Snowflake Cloud Data Platform
Snowflake works on all of the above cloud platforms. The solution loads raw data from JSON, Avro, and XML sources. Snowflake supports updates, deletes, analytical functions, transactions, and complex joins. It requires no infrastructure or management. The columnar database engine crunches data, processes reports, and runs analytics.
The migration of data and infrastructure to the cloud has been a long time coming, and simplifies many operational costs for businesses. However, that doesn’t mean that it’s a perfect solution:
Many companies see DevOps as a challenge rather than an opportunity. An opportunity to boost your software development process. Adopting DevOps requires addressing challenges like:
Our DevOps consulting services support the DevOps cultural change in your organization along with the DevOps tools as you progress towards DevOps principles and software development excellence.
