What is a data lake?
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.
What are the cloud data lake platforms?
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.
What are the data lake challenges?
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:
- Data ingestion - The biggest challenge for cloud data lakes is actually getting data into the cloud. It's not only difficult but also costly when it occurs repeatedly.
- Data quality - Because data lakes can support any type of raw data, maintaining them can be laborious. It can lead to data swamps. A data swamp, full of badly formatted data, is useless to a business and is difficult to clean up.
- Data governance - You want all departments to use data to make decisions, but that doesn't always happen. Your IT or data science teams may be swamped with data queries, causing a bottleneck in business workflows.
What are the benefits of building data lakes in the cloud?
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- Resistance to change - DevOps consulting companies bring new DevOps tools, so people should be open to change.
- Environment provisioning - Often agile enterprise software development requires multiple staging environments for manual testing.
- Replacing or modifying older apps - microservices architecture opens up the doors to faster development and quicker innovation.
- No DevOps center of excellence - DevOps adoption requires building a team of pro-DevOps software developers who can work as influencers within the organization.
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.
