Do you know what is the success factor for today’s small and medium-sized businesses? It’s about how they embrace data-driven decision-making! For that, SMBs deal with vast amounts of data and use data analytics to receive valuable insights. But how should they handle the data?
“AWS Data Lake offers a centralized repository to store and manage structured and unstructured data. SMBs can directly use Data Analytics through the Data Lakes using various Cloud BI tools and analytics services”.
Amazon Web Services provides a broad selection of analytics services that fulfill the data analytics requirements of modern SMBs. Do you want to unlock the potential Data Lake and AWS Analytics Services? Let’s reinvent your business with data!
Why Do SMBs Need Data Lake and Analytics Services?
Every SMB generates data and uses it to make better decisions. Companies that successfully generate value from their data tend to outperform their competitors. AWS Data Lake and Analytics offer companies the right features and capabilities to conduct different types of Data Analysis. They leverage Data Lakes to maximize end-to-end data insights with data ingestion and manipulation. As a result, SMBs can identify opportunities for potential business growth.
Top Benefits Of Leveraging Data Lakes On AWS
Let’s explore the main advantages of storing AWS data in Data Lakes:
Store All Data In A Central Location
Amazon S3 is the best resource for AWS Lake Formation. It offers durability and availability of all organizational data from a centralized location. Plus, Amazon S3 can scale without limit. Hence, AWS consultants can advise businesses to store their data in Amazon S3 buckets.
Increase Innovation
Data is easily accessible from S3 buckets for analysis. Hence, organizations can quickly accelerate innovation using the predictive analytics capabilities of ML models on AWS.
Use The Best Analytics Tools
AWS has purpose-built analytics services that Data Lake users can utilize to extract data insights faster. SMBs can select and optimize the most appropriate tool based on the requirements.
Eliminate Server Management
As AWS manages the entire infrastructure, organizations can select serverless options for data analytics. It enables them to focus more on leveraging data analytics and generating the required insights for the business.
The Essential Elements Of AWS Data Lake To Consider For The Analytics Solution
Before diving into data analytics, SMBs need a solid foundation for managing data. As Data Lakes provide the infrastructure for storing and securing data at scale, it’s ideal for creating an optimal Data Analytics solution. The following section highlights the main capabilities of Data Lakes that SMBs must consider for building their analytics platform:
Machine Learning
Organizations can integrate AI/ML models with Data lakes. It allows them to generate unique types of data insights using AI-powered systems. It helps SMBs innovate better solutions using reporting insights driven by historical data. Further, organizations can make accurate predictions to reduce operational overhead and achieve optimal results.
Data Movement
Irrespective of the source, importing or moving any AWS Data in real-time is easy. Data lakes allow organizations to transform data in its original format from one system to another seamlessly. Organizations can use AWS analytics solutions to send queries to the data and receive the required response accordingly. This process allows them to scale the data using data structures and schema.
Securely Store and Catalog Data
Data lakes make storing relational or non-relational data as easy as possible. So, organizations can quickly move their operational databases to the cloud with the existing data inside them. Further, managing application data collected from IoT devices or mobile applications is easy. It enables organizations to use cloud computing and understand which data needs higher security. Later, they can perform data crawling and cataloging accordingly.
Data Analytics
The “purpose-built analytics” feature enables data scientists and analysts to access data using purpose-built analytics tools. The diverse roles allow them to run analytics jobs quickly and easily. Developers are free to use any open-source tool like Spark or Hadoop. It enables them to utilize data warehousing and run data analytics without transferring data to separate analytics systems.
AWS Data Analytics Solutions: Extracting Insights From Data Lakes
Once you store data in the AWS Data Lake, the next step is to extract valuable insights using data analytics solutions. AWS offers different analytics tools that cater to various analytical needs:
Amazon Athena
Athena is a serverless AWS query service that allows SMBs to analyze data in their data lake using SQL. It requires no installation setup. You can quickly process large datasets using this interactive service. Further, you can gain insights by running ad-hoc queries and aggregating data on the fly.
AWS Glue
It is an ETL (Extract, Transform, Load) service that automates the data preparation and transformation process for advanced analysis. It helps SMBs clean and structure data by preparing it for further study in other AWS services.
Amazon QuickSight
QuickSight is a cloud computing business intelligence (BI) service that enables SMBs to create interactive dashboards and reports. Non-technical team members can easily use this service and explore advanced data insights.
Common Challenges and Considerations For SMBs
While Data Lakes and Analytics offer immense benefits, SMBs should be aware of the following challenges:
- Data storage and processing costs can add up as the infrastructure scales gradually. So, SMBs must monitor and optimize resource usage to avoid unexpected expenses.
- Establishing clear data governance policies and practices and maintaining data quality is a real challenge.
- SMBs may need to hire AWS consulting companies to maximize the utilization of Data Analytics.
- Integrating data from external/internal sources can be complex. So, SMBs should plan their data architecture carefully.
Getting Started With Data Lakes & Analytics On AWS Cloud
Data lakes are now crucial for SMBs to make the most of big data and analytics. Here is how to get started with Data Lakes on AWS:
- Move To AWS: If you have existing databases, migrate them to the AWS cloud. Eliminate dependency on on-premise infrastructure and adopt cloud solutions from Amazon.
- Determine Data Sources: Create a catalog containing the details of all your data sources, including databases, non-relational files, log files, etc.
- Choose A Storage Solution: You can choose AWS S3 Storage Services for your AWS Data Lake and store large volumes of data.
- Set Up Data Ingestion: Establishing data flow from various sources is crucial. You can use AWS Data Pipeline or Glue to transform and extract your data.
- Choose A Data Analysis Solution: After you store the data, it’s time to drive insights from it! You can use Amazon EMR or AWS Redshift to perform data analysis.
- Set Up Data Governance Policies: Don’t overlook the security aspect. AWS Lake Formation can secure data transfer or extraction processes and enable data encryption.
- Make Data Accessible: It’s essential to keep the data handy. You can use Amazon QuickSight to visualize data.
We hope that AWS consultants can now leverage the capabilities of Data Lakes and AWS Analytics Services. SMBs can now store and process their organization data cost-effectively with data lakes. So, it’s time to make data-driven decisions using data insights and drive growth!
FAQs
#1 What are AWS analytics services?
AWS analytics services cover three main solution areas:
1. Advanced analytics (Amazon Athena, Redshift, etc.)
2. Data management (Amazon Glue, Lake Formation, etc.)
3. ML and predictive analysis (Amazon SageMaker, Deep Learning AMIs)
#2 Why should you build a data lake on the cloud?
AWS Data Lake supports Amazon S3 buckets as the ultimate storage solution. Data Lakes make storing and processing data to/from various sources easy for SMBs. It’s easy to scale the analytics solutions on the cloud that combine different data analytics approaches.
#3 Are Data Lakes Different from AWS Data Lab?
Yes, Data Lakes and AWS Data Labs are different concepts. While Data Lakes are storage repositories, AWS Data Lab is a collaborative workspace where data engineers can collaborate to explore and experiment. Hence, it’s more about the process and collaboration than a specific service.
#4 Can I migrate to fully managed AWS analytics solutions?
You can migrate your existing relational/non-relational databases and analytics services to AWS Cloud. If you need expert guidance, you can hire professional AWS consulting companies like Algoworks, specializing in Cloud Migration. Otherwise, you can join Amazon’s Database Freedom program, which offers expert advice, tooling consultancy, and financial incentives.
#5 How do Data Lakes enable unified data access?
Data Lakes enable unified data access to SMBs by:
- Centralization: They store diverse data types in one location.
- Schema-on-Read: Data remains in its original format, allowing flexible querying.
- Metadata Catalogs: Catalogs organize and index data for easy discovery.
- Querying Tools: Users can use various tools, like SQL queries, for analysis.
- Data Consistency: Data remains consistent across different users and applications.
- Scalability: They can handle massive datasets, accommodating growing needs.
This unified approach simplifies data access and analysis for improved decision-making.
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