How to Manage an Object Storage Data Lake
Tags: datascience, database, storage, datalake
Published: 2021-01-05 07:07:28

You might have dealt with computer data storage mechanisms like file systems and block storage. The file system usually saves data in files, while block storage stores it in blocks within tracks and sectors.

There's another type of data storage system in computers called [object storage](https://lakefs.io/object-storage/). What it does is that it stores your data as an object with some metadata and unique identifiers. The main advantage of object storage is that it allows you to store unstructured data easily and intuitively.

The Timeline of Object Storage
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As the adoption of AI becomes widespread, there's also an increased demand for solutions where we can store our data without worrying about the techniques needed to clean it or extract some insights out of it.

Object storage data lakes thus began as a place to dump the data irrespective of its type. It provided an easy to manage and scalable solution for companies to store raw data there. Notable Cloud providers like IBM, AWS, Azure, and GCP provide Object Storage solutions that make it easier for you to search and query to get your desired data from the data lake. 

Managing Object Storage Data Lakes
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Management of Object Storage Data Lakes is a primary concern for many companies. Let's explore some solutions that make it easier for you:

### Ready-Made Object Storage Solutions

Everyone knows that Amazon is an industry leader for cloud computing-related solutions. So, we do have [Amazon's Simple Storage Service](https://aws.amazon.com/s3/), commonly known as S3. It allows you to retrieve and save your data by a simple web interface leveraging the fast, scalable, and performant infrastructure that Amazon uses to run its global network.

Others are Google Cloud Storage by Google, Azure Blob Storage by Microsoft, IBM's Cloud Object Storage, and Alibaba Object Storage Service. There are other options by smaller players in the market like Cloudian, Zadara Storage, Wasabi Hot Cloud Storage, and Aura Object Store.

These solutions take care of all performance- and scalability-related issues and provide you a high-level API to interact with your data seamlessly, leveraging the power of these platforms. Usually, the difference between cloud providers is in terms of prices and downtime. So, if you have a reasonable budget and want the best uptime, you can go with top providers like AWS, Azure, and Google. However, if it's not an issue, then you can choose a cheaper provider.

Different Tools Used
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There are many tools capable of managing Object Storage Data Lakes effectively. Here are a few:

###[Apache Hadoop](https://hadoop.apache.org/)

![A screenshot of Apache Hadoop's website](https://dev-to-uploads.s3.amazonaws.com/i/61324unmquos95zg9j8e.png)

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library can detect and handle failures at the application layer. What you get is a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Hadoop provided a solution to manage the big data workloads that traditional RDBMS (Relational DB Management Systems) cannot handle efficiently.

###[Apache Spark ](https://spark.apache.org/)

![A screenshot of Apache Spark's website](https://dev-to-uploads.s3.amazonaws.com/i/74kf17smxxj3ubstp2k6.png)

Apache Spark is an analytics engine built for processing large datasets. It's built on the top of Hadoop MapReduce and further extends it. The main advantage of using Spark is that it not only supports the Map and Reduce but also supports Queries, Machine Learning, Streaming Data, and Graph algorithms.

Those four features also form the core Spark components, namely Spark SQL, Spark Streaming, MLlib, and GraphX.

###[LakeFS](https://lakefs.io/)

![A screenshot of LakeFS' website](https://dev-to-uploads.s3.amazonaws.com/i/xg3tgr7aetvaxbjnj49t.png)

lakeFS is an open-source platform that delivers resilience and manageability to your existing object-storage based data lake. With LakeFS, you can build repeatable, atomic, and versioned data lake operations -- from complex ETL jobs to data science and analytics.

The best thing about LakeFS is that it integrates seamlessly with your existing tech stack and tools like Hive, Mahour, Spark, or whatever you are using.

Conclusion
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We usually need different toolsets to manage and scale data lakes efficiently. Although the underlying concepts are almost similar, the big data toolset has other APIs which every practitioner needs to master.

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