One common challenge encountered by developers and data professionals in the area of data processing and analysis is whether to import data directly into a database or store it as.csv files before loading. This decision is crucial considering it concerns data continuity, error logging, and data retrieval in the event of a system failure.
The
standard design pattern is to bring in data into a blob storage called raw,
standardised it into parquet or any other columnar format, and then load it
into a database system, or if using spark delta lake, build a delta table with
location referring to storage.
Blob
storage is often preferable since it keeps things flexible and makes debugging
easier. Particularly if you can compress the data, which can easily save 90+%
on egress, ingress, and storage costs. However, if the data from the API is
little enough, it doesn’t really matter.
It doesn’t really matter if you’re constructing a data
warehouse in a relational database like Oracle Exadata (engineered platform for
Oracle).
Having said that, we typically standardise the incoming
data JSON, CSV, XML, mainframe.dat files or SAS b7dat files, then the first
step is to standardise into a columnar format like parquet/orc/iceberg as it
offers better compression and thus saves space and money, then the second step
is to standardise if you want to run ml modes iteratively in the future.
Then you have data saved in the correct format, and csv
format does not inform you what data types are when you look at the data,
however in parquet format data types/schema travel with the data, so you do not
need to explain the schema to the target consumer. And you have the added
benefit of keeping the data in an open-source format, so if you decide to shift
to another platform tomorrow, you will have data available in a consumable
manner, eliminating the need to go source.
Opinions —
Case 1: While some claim that simply
importing data into a database system is more efficient,
Case 2: Others preference the dependability
and flexibility of storing data as.csv files in blob location before moving it
into the database systems.
In this post, we’ll look at the advantages and
disadvantages of each strategy, as well as why one could be preferable to the
other.
Case 1: Loading Data Directly to a
Database System
Loading data directly to a database has its merits. It
eliminates the need for an intermediary step of saving data as .csv files,
allowing for a more streamlined data processing pipeline. Here are a few
reasons why some developers prefer this approach:
1. Efficiency and Performance — By bypassing the step of
saving data as .csv or .json or .parquet files, loading data directly to a
database system can be faster and more efficient. Databases are designed for
data storage and retrieval, making them an excellent solution for dealing with
massive amounts of data. Furthermore, databases frequently have powerful
indexing and query optimisation algorithms that may greatly accelerate data
retrieval and analysis.
2. Real-Time Data Updates — Adding data directly to a
database helps ensure that your analysis is always based on the most up-to-date
information if your data source is constantly updated in real-time. This is
especially handy when working with time-sensitive data or when the data must be
updated often.
3. Data Integrity and Consistency — Data integrity restrictions are
enforced by databases, ensuring that the data being loaded complies with
predetermined rules and standards. You may use these limitations to preserve
consistency and correctness in your data by immediately importing it into a
database. This is especially true when working with data from many sources or
clients.
Case 2: Saving Data as raw file before
ingestion into database system
While loading data directly to a database system has its
advantages, there are also compelling reasons to save data such as .csv or
.json or .parquet files before loading. Let’s explore some of these reasons:
1. Data Continuity and Backup — One of the main concerns with
loading data directly to a database is the potential risk of data loss in case
of system failures or other unforeseen circumstances. By saving data as .csv
files, you create a backup that can be easily accessed and restored if needed.
This provides an additional layer of data continuity and ensures that you have
a copy of your data even if the database becomes inaccessible.
2. Easy Error Logging and Data
Validation — Saving
data as .csv files allows for easy error logging and data validation. When data
is loaded directly into a database, it can be challenging to identify and track
errors that occur during the loading process. However, by first saving data as
.csv files, you can easily log any errors encountered and validate the data
before it is loaded into the database. This can help in identifying and
resolving issues early on, improving data quality and accuracy.
3. Flexibility with Multiple Clients
and Data Sources — Saving
data as .csv files provides a standardized way to load data from multiple
clients and data sources. Different clients may have their own data formats or requirements
and saving data as .csv files allows for seamless integration and
compatibility. It provides the flexibility to transform and manipulate data
before loading it into the database, ensuring that it aligns with your specific
business needs.
As
we all know, API and database rates, availability periods, and so on will
differ. If the database is down for maintenance, for example, you can simply
keep the API scraper running and backfill when the database comes back up.
The Best of Both Worlds: A Hybrid
Approach
Rather than choosing between loading data directly to a
database or saving it as .csv files, a hybrid approach can offer the best of
both worlds. This approach involves leveraging the strengths of both methods to
create a robust and flexible data processing pipeline.
Here’s how it
can work:
1.
Extract
data from the source and save it as .csv or .json or .parquet files files in a
designated blob storage. This provides a backup and allows for easy error
logging and data validation.
2.
Standardize
and transform the data into a more efficient columnar format (such as parquet)
within the blob storage. This improves data storage and retrieval performance.
3.
Load
the transformed data from the blob storage into the database. This ensures data
continuity, real-time updates, and takes advantage of the database’s indexing
and query optimization capabilities.
By adopting a
hybrid approach, you can address the concerns of data continuity, error
logging, and performance while maintaining the flexibility to handle different
data sources and clients.
Keep
the data in its original form, together with the necessary metadata (what query
when against what API, etc.). AFTER THAT, go to an organized storage. Storage
is inexpensive, and you now have independent concerns that are auditable and
re-playable.
Aside
from that, we’ve found it quite useful for reverse engineering undocumented
data sets because you can search for known values and discover the column name
right next to the value. In the database, however, you must know the column
name to search.
Conclusion
When it comes to loading data directly to a database or
saving it as .csv or .json or .parquet files, there is no one-size-fits-all
approach. The decision should be based on your specific requirements, data
characteristics, and business needs. While loading data directly to a database
offers efficiency and real-time updates, saving data as .csv files provides
data continuity, easy error logging, and flexibility with multiple clients and
data sources.
Consider the nature of your data, the frequency of updates,
the risk of system failures, and the need for data validation when making this
decision. In some cases, a hybrid approach that combines the benefits of both
methods may be the best solution. Ultimately, the goal is to create a robust
and reliable data processing pipeline that meets your organization’s unique
needs.
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