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Data Lake: Single,
Shared Storage at Scale

With hundreds of apps deployed on the cloud and on-premises, more data is generated today than enterprises know what to do with. Some get acted upon, the rest are thrown away. Foresighted organizations choose a different path—they invest in data lakes.

Data Lake: Single Shared Storage at Scale

A Shared Repository for All Your Data

Graphical representation of a data lake
Constraint-free

Stores all kinds of data—static, streaming, structured, and unstructured.

Unfiltered Data

Data is ingested “as is.”

Inexpensive

Storage is decoupled from expensive computing.

Organized Zones

Organizing data into zones makes data easy to access and govern.

Multipurpose

Supports ETL, ad hoc queries, advanced analytics, and all kinds of data experiments.

Self-Service Model

With relevant tools, users can self-serve data from the data lake.

Infrastructure Optimized for Big Data

Infrastructure
Optimized for Big Data

The data warehouse (DW) was a dependable workhorse when enterprises dealt mostly with structured data from operational systems. Enterprise applications have diversified since and so has data. The data from IoT apps, web, and social media are too unruly to fit into the predefined schema of a data warehouse. A data lake is uniquely qualified to store and process data with or without accompanying schema.

Store and Access at Ease

Data lake’s schema-on-read architecture makes it ideal for handling a variety of big data. Schema is applied only at the point of interaction, which allows users to explore data in innovative ways.

Break the Data Silos

As the number of applications multiplies, it can compound the problem of silos. Data lake solves this in one fell swoop as the entirety of enterprise data (both historical and real time) can be stored, processed, combined, and analyzed in a single repository.

Scale Economically

As data grows in volume, storage and processing capacities have to be scaled up. This is easier on a data lake, which consists of cost-efficient commodity hardware that can be scaled to thousands of servers on-premises or in the cloud without impacting performance.

Do More with Data

With holistic and up-to-date data storage, enterprises can harvest more value from their data. Data scientists can build, test, and run machine learning models while business analysts can run their own queries on the data.

Store and Access at Ease

Data lake’s schema-on-read architecture makes it ideal for handling a variety of big data. Schema is applied only at the point of interaction, which allows users to explore data in innovative ways.

Break the Data Silos

As the number of applications multiplies, it can compound the problem of silos. Data lake solves this in one fell swoop as the entirety of enterprise data (both historical and real time) can be stored, processed, combined, and analyzed in a single repository.

Scale Economically

As data grows in volume, storage and processing capacities have to be scaled up. This is easier on a data lake, which consists of cost-efficient commodity hardware that can be scaled to thousands of servers on-premises or in the cloud without impacting performance.

Do More with Data

With holistic and up-to-date data storage, enterprises can harvest more value from their data. Data scientists can build, test, and run machine learning models while business analysts can run their own queries on the data.

Building Well-Managed Data Lakes

There are two components to a well-managed data lake. One is the technology stack; the other is data governance. The right stack makes it a well-orchestrated repository, good governance makes it a well-managed one. Depending on your choice of cloud/on-premise infrastructure and business requirements, our engineers will design, set up the stack, and develop governance systems to create a fully functional data lake for your enterprise.

The technology stack for a well-orchestrated data lake consists of various storage and data processing tools in the center and data ingestion and access tools on the edge.

Data Ingestion

Data is ingested in their native formats in batches or streams. The data is tagged with its metadata to make it easy to discover and govern after it enters the data lake. The tags capture the data’s source, size, format, quality, provenance, sensitivity, last accessed date, etc. Data is then validated and routed to appropriate zones within the data lake.

Storage

Data lakes depend on big-data storage infrastructure that ensures high availability and horizontal scalability. Based on requirements, different storage mechanisms like Object Stores (like Amazon S3) and HDFS are adopted. Cost optimization is achieved by moving less frequently used data to low-cost, high latency data storage.

Processing

The data is processed in batches or streams depending on the nature of data ingested, the use case, and latency expectations. A lambda architecture with batch and speed layers can support both types of processing, balancing throughput and latency requirements. Tools such as Spark, Storm, etc., have highly evolved to offer massive parallel processing with varying trade-offs.

Access

The channels for access can vary from DB connectors for stored data to message brokers for streaming data. Metadata tags and catalogs along with standardized access channels make it easier for business analysts to self-service their data needs. With the addition of visualization tools such as Tableau and Qlik, they can easily explore the data and derive insights faster.

Organizing Data Lakes

Organizing data lake into different zones improves usability and helps secure sensitive data. Typically, it is organized into four zones. Additional zones may be added based on data ingestion mode, access privileges, governance practices, etc.

  • Landing zone is where the raw data is stored.
  • Production zone stores cleaned and curated data that end-users can access.
  • Dev zone is for processing data to make it production-ready. It can also be used as a sandbox for exploratory data analysis by data scientists.
  • Sensitive zone has all the sensitive data so it can be tightly governed and safeguarded.
Representation of how a data lake can be organized into different zones

Securing Data Lakes

Role-based access control and other security measures are indispensable for a shared repository such as the data lake. Organizing data into separate zones is the first step in ensuring security. Masking, tokenization, and encryption are also applied to data in different zones to protect it from unauthorized access. Compliance with data governance policies such as GDPR and CCPA is enforced through audit routines. At the transactional level, compliance is tracked using log monitoring systems.

Data Ingestion

Data is ingested in their native formats in batches or streams. The data is tagged with its metadata to make it easy to discover and govern after it enters the data lake. The tags capture the data’s source, size, format, quality, provenance, sensitivity, last accessed date, etc. Data is then validated and routed to appropriate zones within the data lake.

Storage

Data lakes depend on big-data storage infrastructure that ensures high availability and horizontal scalability. Based on requirements, different storage mechanisms like Object Stores (like Amazon S3) and HDFS are adopted. Cost optimization is achieved by moving less frequently used data to low-cost, high latency data storage.

Processing

The data is processed in batches or streams depending on the nature of data ingested, the use case, and latency expectations. A lambda architecture with batch and speed layers can support both types of processing, balancing throughput and latency requirements. Tools such as Spark, Storm, etc., have highly evolved to offer massive parallel processing with varying trade-offs.

Access

The channels for access can vary from DB connectors for stored data to message brokers for streaming data. Metadata tags and catalogs along with standardized access channels make it easier for business analysts to self-service their data needs. With the addition of visualization tools such as Tableau and Qlik, they can easily explore the data and derive insights faster.

Organizing Data Lakes

  • Landing zone is where the raw data is stored
  • Production zone stores cleaned and curated data that end-users can access.
  • Dev zone is for processing data to make it production-ready. It can also be used as a sandbox for exploratory data analysis by data scientists.
  • Sensitive zone has all the sensitive data so it can be tightly governed and safeguarded.

Securing Data Lakes

Role-based access control and other security measures are indispensable for a shared repository such as the data lake. Organizing data into separate zones is the first step in ensuring security. Masking, tokenization, and encryption are also applied to data in different zones to protect it from unauthorized access. Compliance with data governance policies such as GDPR and CCPA is enforced through audit routines. At the transactional level, compliance is tracked using log monitoring systems.

Prevent Data Swamps

Prevent Data Swamps

Without good governance, a data lake can turn into a morass of unusable data. When data enters the lake without a verifiable record of its quality and lineage, it degrades the whole lake. Governance policies and structures designed by our experts help your data lake to flourish as the single source of truth for your organization.

Setting up and enforcing standards for data ingestion, storage, processing, and access ensures that all business users get reliable data from the data lake. Metadata management is another integral part of data lake implementation that makes it reliable and available. It empowers users to search and locate data for analysis independent of IT support.

Do You Need a Data Lake?

Data lakes may be inexpensive by design but that does not make it the right data management solution for every organization. For some organizations, a data warehouse may be more effective and for some others, they both may be complementary. There is considerable effort involved in building a data lake, so it has to be a considered choice based on individual situations. There is also the question of payback. Without a clearly defined purpose, a data lake could quickly become a corporate liability rather than an asset.

A few questions to guide
your decision:
  • Does your organization deal with massive amounts of complex multi-structured data?
  • Does the rate of data generation change rapidly?
  • Do you have varying data retention requirements?

When it comes to designing and implementing successful data lake solutions, there are major managerial and technical decisions involved: setting business goals, selecting the technology stack, establishing systems for cost optimization, monitoring security and performance, and governance. Such a system can serve your enterprise well into the future. Leverage the technical heft of our experienced team of cloud consultants, big data architects, and engineers to build that system for your enterprise.

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