5 Useful Tips for Big Data Migration
Most businesses currently work with Big data. If you are one of them, you might consider a modern data warehouse, a perfect fit for the cloud-centric organization. Big data is a huge and rich system consisting of unique SQL syntax, terabytes of data, thousands of tables, views, specialized code and data types. Big Data which was previously untapped, now through the help of advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing is harnessed for relevant business insights. Considering the complexities involved with big data migrations, companies fail to achieve their planned goals.
To ensure a successful migration journey, avoid these common pitfalls:
Migrating Big Data at One Go
It is common for companies with traditional data warehouses to perform the whole process of data migration at one go. This will reduce cost and improve performance. But we cannot ignore the high risk of migrating the entire data warehouse at once. To lessen the risk, shift the workloads in a phased manner, reducing the burden on the legacy system.
Check Destination Compliance
Critical applications used in the business contain a significant volume of complicated logic. It will involve the most demanding workloads to migrate this complex data to a new platform. Customers have also contributed some of the logic using user-defined functions or procedures. In this scenario, the destination database, data lake or data warehouse should comply with SQL, Spark, JDBC/ODBC, and automatically converting BTEQ scripts, procedures and macros.
Do Not Get Locked-In
The automated data migration tool you will select should always provide the flexibility and functionalities your business requires. It should allow you to move all your data at once and also in phased stages. Companies having privacy or compliance demands prefer to store some data on-premises. But, whatever may be your selection, do not get locked in without any flexibility.
All Cloud Data Warehouses Are Not Created Equal
All Big data warehouses are not designed to provide a fully managed service. Most cloud data warehouse do not cater to modern challenges. Though several cloud data warehouses are inexpensive to get started, there will be a huge monthly subscription bill when you start running full production workloads. Performances also slow down as the volume of users increases.
A Thorough business assessment is Mandatory
A successful data migration journey demands a thorough assessment of the legacy system you are using for your big data environment. You might have invested a lot of time and logic into your Big data platform. There will be a significant amount of junk data: tables, queries, and workloads irrelevant to the business was created over the period. Moving these objects during the migration will be a waste of time and storage.
Hence, reduce the risk involved in data migration by using an automated tool. These tools analyze your Big data warehouse logs to gain a complete picture of your current records. The tools identify drawbacks considering numerous factors and decide prioritization for data migration.
Daton is an automated data pipeline which extracts data from multiple sources for replicating them into data lakes or cloud data warehouses like Snowflake, Google Bigquery, Amazon Redshift where employees can use it for business intelligence and data analytics. It has flexible loading options that will allow you to optimize data replication by maximizing storage utilization and easy querying. Daton provides robust scheduling options and guarantees data consistency. The best part is that Daton is easy to set up even for those without any coding experience. It is the cheapest data pipeline available in the market. Maintain a hybrid cloud data platform easily with a data pipeline like Daton.
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