Ultimate Guide on the Best Data Ingestion Methods for Data Lakes

Big Data offers an open ground of fruitful opportunities and unprecedented challenges that can only be realized with a good Data Ingestion Framework. This framework should play a pivotal role in the Data Lake Ecosystem by establishing data as an asset strategy and churning out enterprise value.

It should securely connect to your Data Sources, capture the changes, and replicate them in the Data Lake, hence maintaining consistency and up-to-date information for proactive analytics.

Data Lake empowers analytical models to churn out the real value of “data” irrespective of its format. It continues to remain a highly agile and data-centric approach for Big Data Analytics.

In this ultimate guide on the best Data Ingestion Methods for Data Lakes, we discuss the techniques and best practices for bringing structured and unstructured data into your Data Lake.

What Is Data Ingestion?

Data Ingestion is a process of importing data from one or more sources and transferring it to a common destination (target) for analysis. Your sources can include Excel sheets, database tables, SaaS data, IoT, legacy documents, and many more. The destination or target can be a document store, database, Data Lake, Data Warehouse, etc. 

Integral to the Extract, Transform, and Load (ETL), a simple Data Ingestion process may comprise one or more Data Transformations that filter or enrich data before writing it to the destination. This leads to reduced redundancies, fewer inaccuracies, and overall improved data quality.

To gain in-depth information on Data Ingestion, we have a separate guide for you here – What is Data Ingestion? 10 Critical Aspects. You can also visit these helpful resources for more information about Data Transformation and ETL.

• What is Data Transformation? : A Comprehensive Guide
• ETL Incremental Loading 101: A Comprehensive Guide

What Is Data Lake?

A Data Lake is a common repository to store many types of data. You can store structured, unstructured, or semi-structured data at large scale or small scale in their native format, as opposed to a DDL-defined schema that is required in a Data Warehouse.

Data Lakes store raw copies of your source data as well as transformed data in a flat architecture. In a flat architecture, you gain the benefit of storing Big Data with flexibility and cost-efficiency, but at the expense of non-relational data.

Data Lakes associate unique identifiers and metadata tags for faster retrieval of such disparate information.

At a high level, A Data Lake consists of four layers, namely:

• Data Acquisition, which lays the framework for acquiring data from different sources, orchestrates ingestion strategy and builds the Data Lake.
• Data Processing, which runs user queries and advanced analytics to derive meaningful information such as recommendations, and business insights, including Machine Learning. 
• Data Analysis is where data is analyzed for easier accessibility on demand. 
• Data Storage stores the analyzed data in appropriate Data Storage Systems.

While Data Lake promises flexibility and improved governance, storing too much unknown, irrelevant, or unnecessary data causes them to become a “Data Swamp”.

This takes away the benefit of finding data quickly and makes your data hard to use. To prevent Data Lakes from turning into Data Swamps, make sure to perform frequent checks and periodic cleansing of data, a process also known as Data Auditing.

Advantages of Using Data Lakes

• Unified View on Data: Companies store and use business data in a wide variety of databases, CRM platforms, and Sales & Marketing applications. A Data Lake unifies all your business data into one single unified view to help your users gain a holistic view and analyze the full breadth of it. 
• No Predefined Schema: Storing data in a Data Lake is a lot easier than in a Data Warehouse since Data Lakes don’t have a predefined schema. It’s not necessary to define the schema before writing data in Data Lakes, and users can store their data in native or raw formats. Data Lakes follow Schema-on-Read, which means that you create schema only at the time of reading data. 
• Simplified Data Management: Data Lakes free you from the complexity and constraints of defining a schema and mapping it accordingly. It stores your data “as is” which can include images, videos, documents, audio, etc.
• Improved Security and Governance: Having a single centralized repository for all your data eliminates problems of data duplication, sharing data from data silos, and difficulties in team collaboration. 
• Better Traceability: Data Lake makes it easy to trace data since the stored data is managed better throughout its entire lifecycle from data definition, access, and storage to processing, and analytics. Moreover, in a Data Lake, you can add as many users as you like without compromising on the performance. 
• Democratized Access: When you use Data Lakes, you get rid of independent data silos and bureaucratic boundaries between business processes. Every user is empowered to access select or all enterprise data if they have the required permissions.

Best Data Ingestion Methods for Data Lakes: Hadoop

In this section, we discuss the best Data Ingestion Methods for Data Lake for bringing both structured and unstructured data into a Hadoop Distributed File System.

Apache Hadoop

Apache Hadoop is a set of open-source tools, libraries, and components for distributed processing of large data sets. It uses MapReduce as its core processing logic for resource management, job scheduling, and job processing.

Hadoop is a proven platform for distributed storage and distributed computing that can process large amounts of unstructured data and is capable of producing advanced analytics. As per your requirements, you can even add more nodes to Apache Hadoop to handle more data with efficiency. 

Hadoop Distributed File System (HDFS)

Hadoop Distributed File System (HDFS) ensures a fault-tolerant and resilient layer on top of the POSIX file system. During a write operation, a file is divided into small blocks and copied across the cluster.

The replication occurs discreetly within the cluster where the replicas cannot be distinctly accessed. When a file is processed in the cluster, all of its copies are processed simultaneously, resulting in improved computing performance and scalability.

In HDFS, one of the simplest Data Ingestion methods for Data Lakes, particularly Hadoop, is to copy your files from the local system to HDFS. You can perform this operation and import CSV, spreadsheets, JSON, or raw text files directly into Hadoop Data Lake. To do so, you can use the “-put” command:

$ hdfs dfs mkdir /user/hdfs/customer_sales_2022

If you want to import multiple CSV from the local system to the Hadoop cluster, you can do so by executing the following command:

$ hdfs dfs -put customer_sales_Q1.csv sales_2022

$ hdfs dfs -put customer_sales_Q2.csv sales_2022

Using this command, you can list your Hadoop cluster files:

$ hdfs dfs -ls /user/hdfs

Once your files are uploaded to the Hadoop cluster, they can be used by Hadoop processing layers such as the Hive Data Storage, Pig script, Mapreduce custom programs, and Spark Engine.

Apache Sqoop

Apache Sqoop or “Scoop to Hadoop” is one of the main technologies that is used for transferring data from structured data sources like Relational Database Management Systems (RDBMS), traditional Data Warehouses, and NoSQL data sources to Hadoop Data Lake.

Apache Sqoop is a native component of the Hadoop Distributed File System (HDFS) layer, and it allows the bidirectional bulk transfer of data from the HDFS.

Advantages of Sqoop Data Ingestion Methods for Data Lake

Listed here are the advantages of using Apache Sqoop for ingesting data:

• Sqoop offloads ETL (Extract, Load and Transform) processing into low-cost, fast, and effective Hadoop processes. 
• Sqoop performs data transfers in parallel, making them faster and more cost-effective.
• It helps to integrate sequential data from the mainframe. 
• Using Sqoop, data from other structured data stores can be Sqooped into Hadoop, which is mainly for unstructured data stores. Using this, you can combine both types of data for various analysis purposes in a more cost-effective and fast manner.
• Sqoop comes with a number of built-in connectors for stores such as MySQL, PostgreSQL, Oracle, etc.
• Sqoop has JDBC connectors as well as direct connectors that leverage native tools for better performance.

Sqoop Data Import

The import tool in Sqoop allows users to import single or multiple tables from RDBMS tables into HDFS using several available connector APIs. When a user provides commands, the import tool imports each row in an RDBMS table into HDFS as a record.

Text data is stored as text files, whereas binary data is kept as sequence files and Avro files, depending on the kind of data.

Here’s a diagram to illustrate how the Sqoop import tool works by importing data from PostgreSQL into HDFS:

Apache Flume

Our second technology in Data Ingestion methods for Data Lake is Apache Flume. Apache Flume is a real-time data transfer technology, to capture and load large data volumes from different source systems to the Hadoop Data Lake.

Apache Flume is primarily used for stream Data Ingestion and also works well for other scenarios when you want to bring log data into Hadoop.

Advantages of Flume Data Ingestion Methods for Data Lake

• Apache Flume is open source, hence free.
• It offers high throughput with low latency. 
• It’s highly extensible, reliable, and scalable (horizontally).
• Installing Flume is cheap and involves low-cost operations and maintenance.
• Flume has built-in support for a variety of source and destination systems.
• It can get your data from multiple servers into Hadoop easily.
• It supports different data flows like multiple-hop, fan-out, fan-in, and so on.

Flume ingests real-time data from various business applications and then transfers it to the Hadoop file system for storage and analysis. This is depicted in the image below.

The data collected can be customer behavioral data, page visits, link clicks, location details, browser details, and various other types of information stored in your RDBMS systems.

One of the best advantages of using Apache Flume is that it does not put any pressure on the source system and works completely in a disconnected manner. 

Up next, in this Data Ingestion methods for Data Lakes guide, we discuss the best ingestion methods for Amazon S3 Data Lake.

Best Data Ingestion Methods for Data Lakes: Amazon S3

Amazon S3 is a high-speed, easy-to-use, scalable cloud storage platform with 99.999999999% durability. Amazon S3 proves to be an optimal choice for Data Lake because of its virtually unlimited scalability and wide availability with no barriers to data storage. 

Your businesses can store datasets of sizes ranging from gigabytes to petabytes without compromising performance or pricing. Amazon S3 lets you pay only for what you use pricing, and it seamlessly integrates with AWS and third-party ISV tools for quick Data Ingestion and Processing.

In this best Data Ingestion methods for Data Lakes guide, particularly Amazon S3 Data Lake, we discuss four of the top available tools for Data Ingestion: 

• Amazon Kinesis
• AWS Glue
• AWS Snow Family
• AWS Data Sync

Amazon Kinesis

Amazon Kinesis is a fully-managed, scalable cloud service to collect, process, and analyze real-time streaming data of any size. It can capture data from large, distributed streams like event logs and social media feed and ship it directly to your Amazon S3 Data Lake.

The services availed by the platform can be scaled up and down (which are deployed on EC2 instances) according to the users’ data requirements. You can even configure Kinesis Data Firehose to transform streaming data before it gets stored in your S3 Data Lake.

Some of the transformations Kinesis Data Firehose can perform are conversion of JSON data to Apache Parquet and Apache ORC or using Lambda functions to transform CSV files, Apache Log, or Syslog formats into JSON.

Using Kinesis, your users can easily analyze and transform streaming data because of Kinesis’ native integration with Amazon Kinesis Data Analytics. Amazon Kinesis Data consists of Apache Flink and SQL applications which can help you perform your required operations easily.

AWS Glue

Just like Amazon Kinesis, AWS Glue is a fully managed serverless ETL service to categorize, clean, transform, and reliably transfer data from different source systems to your Amazon S3 Data Lake. It is one of the best Data Ingestion methods for Data Lakes S3 and is cost-effective as well. 

AWS Glue offers  16 preload transformations that allow you and your users to alter ETL processes and meet the target schema. Developers can change the Python code generated by AWS Glue to accomplish more complex transformations, or they can use code written outside of Glue to run their ETL job.

AWS Glue can ingest both structured and semi-structured data in your Amazon S3 Data Lake, Amazon Redshift Data Warehouse, and numerous AWS databases.

The Glue Data Catalog, which is accessible for ETL, Querying, and Reporting, leverages Amazon EMR, Amazon Athena, and Amazon Redshift Spectrum to provide a single view of your data for better understanding and analysis.

AWS Snow Family

AWS Snow Family is a collection of physical devices that can migrate petabytes of data in and out of AWS Cloud without any network requirements. The members of the Snow Family include:

• AWS Snowcone
• AWS Snowball
• AWS Snowmobile

Using these devices, you can transfer data generated from sensors, IoT devices, machines, databases, backups, analytical datasets, and historical records to your Amazon S3 Data Lake easily in a cost-effective way. You can also use AWS Cloud Storage Optimized Snowball and transfer data securely from on-premise storage platforms or Hadoop clusters.

AWS Data Sync

AWS Data Sync is one of the best Data Ingestion Methods for Data Lakes S3. It connects to your destination like S3 Data Lake and simplifies Data Ingestion from on-premise storage solutions and AWS storage services.

Data Sync can quickly copy data from Network File System (NFS) shares, Server Message Block (SMB) shares, Hadoop Distributed File Systems (HDFS), self-managed object storage, AWS Snowcone, and Amazon Simple Storage Service (Amazon S3) buckets, etc. to transfer it securely with end-to-end data validation. 

Up next, in this Data Ingestion methods for Data Lakes guide, we discuss the best Data Ingestion methods for Data Lake Azure.

Best Data Ingestion Methods for Data Lakes: Azure Data Lake

Azure Data Lake is a powerful and feature-rich cloud storage solution from the Microsoft Azure ecosystem. It can seamlessly connect with your operational Data Stores, and Data Warehouses to ingest, store and analyze data using batch, streaming, and interactive analytics.

It also provides features like hierarchical file access, directory- and file-level security, and scalability combined with low cost, tiered storage, high availability, and disaster recovery capabilities.

In Azure Data Lakes, you can perform Data Ingestion using three different ways:

1. Ingestion using pipeline connectors and plugins.
2. Ingestion using integration services like Azure Data Factory.
3. Programmatic ingestion.

In this section on Data Ingestion methods for Data Lakes, specifically for the Azure Data Lake, we will discuss the Azure connectors, plugins, and integration services that can be used to import your data.

Event Grid

Azure Event Grid is an event routing service that sends events from source to handlers. It can connect to any application that you create, pull the events generated by the application, and publish them to multiple destinations or event handlers using the Topic & Event Subscription system.

Event Grid is not a Data Pipeline service since it doesn’t deliver the actual object. However, Event Grid can deliver notifications of the event that has occurred on the publisher to your subscribers.

Azure Data Factory (ADF)

Azure Data Factory is a fully managed orchestration engine built for ETL/ELT scenarios. It helps write Data Pipelines and fetch data from different applications, including Microsoft’s own web services and cloud-hosted applications. It is one of the best Data Ingestion methods for Data Lakes Azure and is cost-effective as well.

Azure Data Factory can ingest data from your sources and write it to Azure Data Lake or other storage platforms. It comes with a code-free user interface with drag and drop capabilities that can be readily used by anyone to create and run their own Data Pipelines

With Azure Data Factory, you also get to avail a spectrum of connectors (more than 90) for connecting multiple on-premise and cloud data sources. 

Connectors and Plugins

There are multiple connectors and plugins available to ingest data into your Azure Data Lake like Logstash connector, Kafka connector, Apache Spark connector, and Power Automate. These connectors help ingest your data from Logstash, Kafka, and Spark clusters respectively.

Power Automate helps you perform preset actions using the query results as a trigger. More information about these connectors and plugins can be found here- Microsoft Docs.

Conclusion

Now that we’ve seen different Data Ingestion Methods for Data Lakes of three of the most widely used options- Hadoop, Amazon S3, and Azure Data Lake, we hope you will be able to ingest data from your data stores into your Data Lake without any problems.

In this informative guide, we looked at the best Data Ingestion methods for Data Lakes and their respective advantages, best practices, and working to help maximize the out-turn.

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Thank you for reading! Post your opinions on learning about the best Data Ingestion methods for Data Lakes like Hadoop, Amazon S3, and Azure Data Lake in the comment box below.