Big Data Integration: Tutorial & Best Practices

Big data integration is a process for ingesting, blending, and preparing data from one or more sources so that it can be analyzed for business intelligence and data science applications. A key to a successful big data integration strategy is understanding that data requires cleaning and comes in different formats, sizes, and velocities. This means that data integration processes must consider all possible combinations that pertain to the data characteristics and sources of interest for a particular use case.

Fundamental questions like these are critical parts of an effective approach to big data integration:

• Where will the data live?
• How efficient are the transformation processes?
• How consistent is the data quality?
• Who should be allowed to access the data?
• How much software development effort can we afford in terms of time-to-value and engineering resources?

This article explores key big data integration concepts and provides recommendations for successful integration based on project needs and engineering resources.

Summary of key big data integration concepts

Starting a big data integration project can often be daunting. To achieve a successful integration, it is essential to create a plan that addresses the following key concepts. 

Essential big data integration concepts

The sections below explain the essential big data integration concepts in detail. 

Identifying data sources

Before diving into the technicalities of data ingestion, organizations should strategically determine which data sources are valuable for their objectives. This step involves:

• Determining internal vs. external data sources: While internal data sources are typically more accessible, their complexity can rise as the organization’s size increases. Larger enterprises often have myriad data sources managed by different teams. On the other hand, external data sources, like APIs, come with associated costs and require an understanding of their data structures and pricing models. For example, the Twitter Search API charges a premium subscription fee that varies between $149 and $2,499 depending on the total number of requests per month. 
• Evaluating data relevance: Schedule introductory sessions with teams or request sample data extracts. This helps with gauging the potential usefulness of a data source before committing resources to integrate it.
• Understanding costs: It’s vital to account for potential costs, especially with external data sources—for instance, some APIs charge based on the volume of data requests.

With a clear idea of which data sources to prioritize, organizations can then delve into the technicalities of data ingestion.

Data ingestion

Data ingestion is a cornerstone of the data integration lifecycle, enabling organizations to collect, process, and centralize data from diverse sources. Here’s a streamlined overview of the process:

1. Accessing data sources: 
   1. Internal sources: Before initiating the ingestion process, obtaining the necessary authentication credentials for internal data sources is vital. Delays can occur due to internal approval workflows or the data owner’s availability.
   2. External sources: Survey external data providers to gain insights into their data structures and potential costs. Some sources necessitate API keys or distinct authentication methods.
2. Extracting data: This involves establishing reliable connections to identified data sources and retrieving the data, often based on specific criteria or filters. A special parser may be needed for non-standard or industry specific data format, such as EDI or FIX files.  
3. Transforming data: After extraction, the data may require transformation to align with the format of the destination or specific business requirements. This can encompass activities like data cleansing, enrichment, filtering, and reshaping. Data masking and hashing may also happen at this stage for compliance.
4. Loading data: The refined data is then ushered into its determined destination, be it a data lake, data warehouse, or another pertinent storage medium.
5. Automation and scheduling: Automation ensures timely and consistent data retrieval. By setting schedules, new or updated data can be regularly ingested, guaranteeing freshness and relevance.
6. Monitoring: Implementing a robust monitoring system for the ingestion pipelines is pivotal. This helps in tracking data flow, assuring data integrity, and swiftly pinpointing and remedying any issues that arise.

An overview of the data ingestion lifecycle.

Deciding between crafting in-house data ingestion mechanisms or employing third-party solutions is contingent on budget, in-house technical prowess, and unique business needs. While third-party GUI tools might present a more user-friendly interface with features such as intuitive connectors, in-house solutions grant greater customization, albeit with a potential increase in maintenance responsibilities.

The backbone of any effective data ingestion process is a sturdy infrastructure. While devices like laptops can serve for preliminary tasks, when it comes to large-scale data ingestion, having a solid infrastructure in place is mandatory to avert potential impediments.

Data transformation

Data transformation involves the application of logic to data using a big data processing framework. Spark is a popular open-source technology for big data processing and transformation, so we will use it as an example to convey data transformation concepts, however it should be noted that Kafka, or other systems can be chosen as well depending on velocity and volume of data. 

Spark provide the following benefits at multiple layers of the big data integration pipeline:

• Data ingestion: If the data source is a database, Spark can be very beneficial by allowing parallel reading of data (depending on the number of allocated CPU cores), which can increase speed.
• Data processing: This can be part of a wider ETL or ELT pipeline, which means Spark can apply transformations after data has been ingested.

Like data ingestion pipelines, data transformation pipelines also need a preconfigured infrastructure. In many cases, the data platform that is selected for data ingestion can also apply as the data transformation platform—this depends on the company’s preferences, or a different tool can be used for each.

Most third-party tools can run both customized code and prebuilt connectors to perform common data engineering tasks, such as dealing with nulls, mapping schemas and data types, blending data from different sources, and aggregating data. 

In terms of deploying these pipelines, the following are the most common market options, sorted in order of complexity (highest to lowest):

• Containerized deployments, such as code deployed as part of Docker containers / Helm charts in a Kubernetes cluster or containerized environments (e.g., AWS ECS / Fargate)
• Managed cloud services, including code deployed on cloud platforms, like AWS Lambda and AWS Glue jobs
• Cloud-based tools with user interfaces, such as Azure Data Factory
• Modern data engineering tools, such as Nexla, that simplify the process and streamline data integration, processing, quality assurance, and governance into one platform accessible via a user interface portal and supported by dozens of prebuilt data connectors.

Data integrity: ensuring quality, security, access, and governance

In a data-driven environment, data’s soundness and reliability are paramount. Data integrity includes ensuring the precision of the information, managing its access, and upholding governance standards.

Fostering a data integrity culture requires a trifecta of rigorous quality checks, well-defined access protocols, and robust governance practices. Only by integrating these facets can organizations genuinely unlock their data’s value and responsibly harness its potential.

While the Data Integration process reads, transforms, and writes data from one system to another, it is a good time to also apply data quality checks and handle data issues prior to writing it out the destination.

Upholding data quality

Real-world data is rife with imperfections. Whether it’s incomplete data entries or inconsistencies between datasets, such challenges can lead to skewed analytics and potentially incorrect business decisions.

For instance, manually entered data is prone to human error, while time-series datasets might present gaps due to unforeseen delays. Having robust quality checks, like comparing customer birth dates across systems or implementing interpolation logic for missing values, can significantly enhance data reliability. Tools like Nexla, which offer built-in data monitoring capabilities, can be pivotal in ensuring data accuracy.

Managing data security

Most managed data storage and data processing solutions enable data encryption by default. However, it is important to check that it’s enabled both at rest (i.e., where data is stored) and in transit (i.e., when loading a data processing result in a database). 

It is also crucial not to hardcode any secrets (such as passwords or API keys) within the code. There are numerous ways to minimize security risks, such as using vault software like AWS Secrets Manager, Azure Key Vault, or Hashicorp Vault.

Managing data access

The sanctity of data often hinges on who has access to it. Through authentication mechanisms, businesses can ascertain the legitimacy of a user’s data access request.

Adopting role-based access controls, such as restricting marketing professionals from accessing financial data, can help mitigate risks. However, special provisions might be required for roles like data scientists, who typically need cross-departmental data.

Innovative approaches, such as the data mesh system, represent the forefront of federated access control, enabling data owners to manage better and control data accessibility.

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Prioritizing data governance

Going beyond just access and quality, effective data governance encompasses a broader set of practices aimed at data privacy, security, and compliance.

Simple practices, like maintaining standardized naming conventions across datasets, can expedite integration efforts and bring about a sense of uniformity. This not only fast-tracks project implementations but also ensures stability across platforms.

Governance also means being proactive. With early warning systems, data teams can quickly address inconsistencies or breaches, ensuring that data policies are always upheld.

Data Destination

The final step for Big Data Integration after ingestion, transformation, and quality checks is to deliver data into its destination, typically a Data Storage from where data can be consumed for Analytics and AI use cases. 

In today’s data-driven world, traditional data storage, like OLAP data warehouses, have their place but may only sometimes be the optimal choice for handling vast amounts of data. These platforms often have structured architectures that limit rapid scalability or flexibility in specific scenarios.

For instance, while expanding an OLAP-based data warehouse in a cloud environment could previously involve significant time and manual effort, today’s cloud-native solutions offer more seamless scaling options. Moreover, earlier data warehouses predominantly supported SQL, limiting the direct utilization of powerful programming languages like Python or R. However, modern data platforms increasingly integrate multi-language support, broadening their capabilities.

The rise of Hadoop, MapReduce, and HDFS provided valuable alternatives with cost-effective storage, diverse APIs, and enhanced parallel processing. Yet these technologies have also introduced challenges, like tightly coupled storage and compute layers, which sometimes led to cost inefficiencies in specific use cases. Organizations need to stay abreast of the latest technologies as the data ecosystem evolves, ensuring that they leverage the most suitable platforms for their specific needs.

Relying on traditional data platforms, such as online analytical processing (OLAP) data warehouses, is only sometimes the best approach for big data. Their strict architectures and lack of efficient scaling options prohibit an effective big data storage and processing experience. 

For example, scaling could mean the addition of an extra physical node to a data warehouse cluster and its reconfiguration in a private cloud environment, which can take weeks. Also, the limitation of using only SQL in data warehouses means that popular programming languages (such as Python or R) and their capabilities (which exceed SQL’s) have been constrained.

Hadoop, MapReduce, and distributed file systems (HDFS) have introduced solutions for cheaper storage, APIs, and faster parallel transformations. However, having a tightly coupled storage and compute layer can have unnecessary cost implications, especially for batch-processing computations. 

The evolution of cloud computing and the proliferation of big data solutions have significantly expanded the horizons of data integration. Enterprises no longer need to be limited by the constraints of traditional systems. Today’s landscape offers diverse solutions catering to different organizational needs, whether real-time processing, deep analytics, or integrating multiple data sources.

Platforms like Nexla provide a user-friendly solution for organizations looking to empower their non-technical teams and democratize data access. Such platforms streamline complex data operations without requiring extensive coding knowledge, enabling business users to quickly integrate and analyze data from various sources. It’s essential to remember that while tools like Nexla simplify many processes, they are part of a larger ecosystem, each tool serving a unique purpose and function.

Decoupling data storage

Decoupling data storage from data processing to optimize costs has been the key issue for Hadoop and traditional data warehouses. What solves this issue and is the most traditional example of data storage intended for big data is a form of object storage such as AWS S3. 

A conceptual example of decoupled compute and storage (Source)

Object storage has the benefit of being able to evolve to a data lake that can grow without any limits on storage, even up to petabytes of data, while also allowing SQL access to the underlying data (e.g., via AWS Athena). Apart from object storage, other modern data warehouse and lakehouse solutions offer this decoupling capability by abstracting the use of object storage and marketing themselves as third-party tools, most notably Snowflake and Databricks Delta.

Data catalog

This key concept allows data consumers to understand what data is available to them. The modern approach to creating a data catalog organizes raw data into data products, as explained in this article. Data products are more than repositories of raw data: They include metadata, data schemas, version control, data samples, and even logic for data transformation.

Another aspect of data products that can be beneficial is data lineage, which involves illustrating what data sources and data processing a column has been through to arrive at its final format as presented in the data catalog. 

Cutting-edge vendors, such as Nexla, take the notion of the data catalog a step further by introducing a private marketplace of data products where data owners can share their data with potential consumers, who can enrich and use it.

Recommendations for big data integration

There are many variables involved in enabling big data integration. In the sections below, we’ll review key recommendations and best practices that can help identify the right solution for each specific use case.

Choose the right tools

The right big data integration tool depends on the use case and business objectives. To ingest and process only megabytes of data per day, a big data platform like Databricks or Spark may be overkill. Instead, it might be enough to process such amounts of data in plain Python with pandas in a small cloud cluster using one of the public cloud providers (e.g., using EC2 or a Docker container running on AWS Fargate). 

On the other hand, if it is estimated that volumes could grow to terabytes or even petabytes, picking a scalable data platform that decouples compute and storage is the best approach to handle these large workloads while maintaining significant optimization.

Plan ahead for where data will live 

Before committing to start a big data integration project, it is essential to define a clear set of rules about where data will live, irrespective of its nature, format, and quantity.

Data can come from different areas, and a widespread mistake in the industry is the introduction of data silos. Without a central management entity, different teams can start building their own pipelines and introducing their own data platforms, effectively creating a non-harmonious, heterogeneous set of complex architectures in what should have been a unified data integration approach. This usually leads to more data silos and downstream issues, such as higher maintenance costs. It also makes it harder to get value out of data because the silos will have different data conventions, like the naming of fields, a non-unified data catalog, or different data access policies that necessitate granting more permissions, which is always time-consuming. These complexities can hinder the performance of data analysts or data scientists.

Know when to build yourself or use third-party platforms 

A clear strategy should define the skill set and the number of people required to form a team that builds and maintains a big data integration platform. Depending on the structure of the organization, this team can be big or small. Maintaining a big data integration platform usually requires engineers in multiple disciplines, but sometimes you might encounter specific individuals who can wear multiple engineering hats and effectively serve many purposes within such a team. 

In general, it’s not only data engineers who are required to build the relevant data pipelines: There is also a need to have cloud engineers who at least have knowledge of DevOps, infrastructure-as-code technologies, and CI/CD pipelines. A team of cloud engineers would make up the backbone of the data integration platform, basically unlocking the value of the work that the data engineers are doing.

If a large data engineering team of this type is not viable, the best approach is to opt for a third-party tool such as Nexla’s data integration platform. This platform manages most of the difficult tasks mentioned above and takes away the need to have a big cloud engineering team. It effectively acts as a platform ready to be consumed by the data engineering team and then the data analysts team. 

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Conclusion

A strategic list of objectives is the first step in planning an enterprise big data integration strategy. There are many concepts to think about beforehand, and it’s important to invest enough time to have a concise and thorough big data integration plan. 

Selecting the right data integration tooling is one of the most important tasks. This should be followed by individual data-related tasks such as researching the data to build data integration pipelines, implementing data quality on the ingested data, and consolidating data formats into a common structure. It is also important to store data efficiently and securely and distribute data to data analysts, who are responsible for extracting business value out of this clean and reliable data.